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In the dark or muddy environments of the ocean, the artificial lateral lines (ALLs) based on bionics are mostly used to detect oscillating obstacles, such as dipole sources. To further expand the function of ALLs, in this article, the active recognition of nonoscillating obstacles is realized by an ALLs based on a pressure sensor array. First, the experimental and simulation platforms with the sensor array are built, and a perception framework is proposed for processing the sensor data. Then, the simulation platform is verified through the pool experiment, and the mechanisms of pressure changes generated by static obstacles in the process of robot active recognition are analyzed. Finally, according to these mechanisms, the obstacles are recognized through a multilayer perception framework that considers the time and spatial context. The recognition results show that the ALLs and the perception framework are effective in the active obstacle recognition of robots. The effects of feature correlation, data volume, and sensor layout are also analyzed. In addition, the data from the pool experiment can obtain satisfactory perception results.

This study utilized Finite Element Analysis (FEA) to establish a time-domain simulation model for the design and evaluation of gradiometers. The simulation model was developed for an innovative two-dimensional (2D) gradiometer structure, specifically designed to connect to the Superconducting QUantum Interference Device (SQUID) for the measurement of magnetocardiographic signals (MCG). The experiment focused on optimizing the horizontal distance of the 2D gradiometer using the simulation model, conducting simulations under shielded and unshielded conditions to measure MCG signals. Simulation results indicated that the optimal performance of the 2D gradiometer is achieved when the horizontal distance is 2.22 times the diameter of the detection coil. Subsequent research simulated the geomagnetic field in shielded and unshielded environments, introducing a second-order axial gradiometer for comparison. The analysis revealed that in the shielded environment, the 2D gradiometer exhibited a noise suppression ratio of approximately 40 dB and a signal restoration rate of 99.86%, while the second-order gradiometer achieved 59 dB and 99.71%, respectively. In the unshielded environment, the noise suppression ratio of the 2D gradiometer decreased to 33 dB, but both gradiometers maintained a signal recovery efficiency of around 99%. This study, employing simulation methods, comprehensively analyzed the temporal characteristics of the gradiometer, significantly enhancing design efficiency and reducing testing costs. It holds crucial guiding value for the development of gradiometers.

Two-dimensional (2D) semiconductors have recently attracted considerable attention due to their promising applications in future integrated electronic and optoelectronic devices. Large-scale synthesis of high-quality 2D semiconductors is an increasingly essential requirement for practical applications, such as sensing, imaging, and communications. In this work, homogeneous 2D GaTe films on a centimeter scale are epitaxially grown on fluorphlogopite mica substrates by molecular beam epitaxy (MBE). The epitaxial GaTe thin films showed an atomically 2D layered lattice structure with a T phase, which has not been discovered in the GaTe geometric isomer. Furthermore, semiconducting behavior and high mobility above room temperature were found in T-GaTe epitaxial films, which are essential for application in semiconducting devices. The T-GaTe-based photodetectors demonstrated respectable photodetection performance with a responsivity of 13 mA/W and a fast response speed. By introducing monolayer graphene as the substrate, we successfully realized high-quality GaTe/graphene heterostructures. The performance has been significantly improved, such as the responsivity was enhanced more than 20 times. These results highlight a feasible scheme for exploring the crystal phase of 2D GaTe and realizing the controlled growth of GaTe films on large substrates, which could promote the development of broadband, high-performance, and large-scale photodetection applications.

The incidence rate of pancreatic tuberculosis (PT) is low, and it is difficult to distinguish from pancreatic malignant tumor before operation. We present 3 patients diagnosed after surgical treatment. All 3 cases involved female patients who were admitted due to primary manifestation of abdominal pain. These patients did not exhibit symptoms such as jaundice or back pain, and their tumor markers were within normal range. The surgical interventions employed included partial resection of the tumor in 1 case, mass resection in another, and pancreaticoduodenectomy in the third. Preoperative puncture biopsy is essential for diagnosing PT, and it should be actively conducted on patients with suspected cases. We summarize several common manifestations of PT to find the possibility of tuberculosis during preoperative evaluation and avoid misdiagnosis and unnecessary surgery.

Smart meters with Non-Intrusive Load Monitoring (NILM) technology is explored as a means of reducing energy consumption and minimizing emissions in residential settings. Various NILM disaggregation algorithms have been proposed, but their effectiveness varies across different scenarios. To address this issue, a method called Data-driven Recommendation Model (DRM) based on meta-learning is proposed in this paper. DRM recommends the appropriate algorithms for various problems using the existing knowledge repository. Besides, a novel meta-feature extractor named Meta-Learning Encoder-Decoder is designed to efficiently process the deep meta-features from a more significant number of training samples, which can be implemented to various meta-learners. Experimental results indicate that the proposed DRM exhibits strong learning and generalization capabilities in terms of the efficiency of load disaggregation for datasets with different characteristics.

Robot-assisted endovascular catheterization is generally performed remotely; interventionists perceive the operation status and control surgical instruments through haptic interfaces. Our main objective is to create a haptic interface that retains interventionists’ prior clinical operating experience, including motion perception, force feedback, torque feedback, tactile sensation, and operating mode. The haptic interface can capture the interventionists’ operation of both catheters and guidewires and generate force and torque to assist the interventionists in performing surgeries on the master side. The tactile sensation and traditional operating mode are retained by adopting real commercial catheters/guidewires as the operating handles and designing a cooperative operation paradigm. A novel sensor-based force generation solution and a torque generation method with a pantograph mechanism were proposed to improve the force and torque feedback accuracy. Laboratory and animal experiments illustrated the good feasibility of the proposed haptic interface. The proposed haptic interface has a good performance and can enable interventionists to utilize their prior accumulated clinical operating experience. This research could provide a reference for the design of haptic interfaces, and the developed haptic interface has the potential for use in robot-assisted endovascular catheterization.

Tumor cells establish a robust self-defense system characterized by hypoxia, antioxidant overexpression, DNA damage repair, and so forth to resist radiotherapy. Targeting one of these features is insufficient to overcome radioresistance due to the feedback mechanisms initiated by tumor cells under radiotherapy. Therefore, we herein developed an engineering biomimetic nanosystem (M@HHPt) masked with tumor cell membranes and loaded with a hybridized protein-based nanoparticle carrying oxygens (O2) and cisplatin prodrugs (Pt(IV)) to target multiple tumor radioresistance hallmarks for enhanced radiotherapy. After administration, M@HHPt actively targeted and smoothly accumulated in tumor cells by virtue of its innate homing abilities to realize efficient co-delivery of O2and Pt(IV). O2introduction induced hypoxia alleviation cooperated with Pt(IV) reduction caused glutathione consumption greatly amplified radiotherapy-ignited cellular oxidative stress. Moreover, the released cisplatin effectively hindered DNA damage repair by crosslinking with radiotherapy-produced DNA fragments. Consequently, M@HHPt-sensitized radiotherapy significantly suppressed the proliferation of lung cancer H1975 cells with an extremely high sensitizer enhancement ratio of 1.91 and the progression of H1975 tumor models with an excellent tumor inhibition rate of 94.7%. Overall, this work provided a feasible strategy for tumor radiosensitization by overcoming multiple radioresistance mechanisms.

The variable operating conditions and hostile environments faced by underwater robots remain a challenge for motion control in unknown environments. In order to improve the capability of the amphibious spherical robot (ASR) in unknown environments, a decentralized hierarchical deep reinforcement learning (DRL) motion control method based on deep deterministic policy gradient (DDPG) for multiple ASRs system is proposed. In the low-level, a DDPG-based motion controller is trained under a compound rewarding to learn to set the configurations of the tilting angle and rotational speed of each thruster at a proper timescale. At the high-level, a planning controller consisting of different action networks is designed to generate a reasonable thrust target to guide the movement of the robot. Specifically, inspired by the artificial potential field (APF) method, the complex underwater motion can be decomposed into several simple virtual forces. Each action network is trained to learn to generate a virtual thrust target component for a specific action. By combining the outputs of several action networks, the distributed cooperative motion control for multirobot systems can then be easily achieved. Finally, the motion control strategy is applied to the physical multi-ASR system, and the experiment results have shown satisfactory performance.

As a promising alternative for minimally invasive vascular surgery, robot-assisted vascular interventional system (VIS) has expressed considerable benefits in preventing radiation, decreasing workload for surgeons, improving surgical environment, and reducing postoperative risk for patients. However, the existing robotic VISs ignore to employ proper haptic feedback for robot-assisted surgery owing to the short of flexible and controllable capabilities of haptic perception. In this work, we develop a dual-instrument operating robotic VIS to manipulate guidewire and catheter, which is capable for multi-instrument surgical tasks. Most importantly, a soft magnetic-actuation-based haptic interface, a prospective solution for haptic sensing based on flexible combination of soft material and magnetic material, is proposed to provide adjustable haptic information for operators. Moreover, a safety operation strategy based on the proposed haptic interface is designed to ensure a safer operating environment for interaction between master side and slave side. Four kinds of experiments are performed to demonstrate the feasibility of soft magnetic-actuation-based haptic feedback and evaluate the tracking performance and operating performance of developed robot-assisted VIS. Results prove the engineering feasibility (haptic sensing range: 0.01–1.49 N) of the proposed haptic interface and the operating effectiveness of developed robot-assisted VIS (maximum linear error is 1.87 mm). Besides, this effort probably has a potential impact on the implementation of soft haptic feedback, research of multi-instrument device, and improvement of safety operation for robotic systems.

Most cases of herpes simplex virus 1 (HSV-1) encephalitis (HSE) remain unexplained1,2. Here, we report on two unrelated people who had HSE as children and are homozygous for rare deleterious variants of TMEFF1, which encodes a cell membrane protein that is preferentially expressed by brain cortical neurons. TMEFF1 interacts with the cell-surface HSV-1 receptor NECTIN-1, impairing HSV-1 glycoprotein D- and NECTIN-1-mediated fusion of the virus and the cell membrane, blocking viral entry. Genetic TMEFF1 deficiency allows HSV-1 to rapidly enter cortical neurons that are either patient specific or derived from CRISPR–Cas9-engineered human pluripotent stem cells, thereby enhancing HSV-1 translocation to the nucleus and subsequent replication. This cellular phenotype can be rescued by pretreatment with type I interferon (IFN) or the expression of exogenous wild-type TMEFF1. Moreover, ectopic expression of full-length TMEFF1 or its amino-terminal extracellular domain, but not its carboxy-terminal intracellular domain, impairs HSV-1 entry into NECTIN-1-expressing cells other than neurons, increasing their resistance to HSV-1 infection. Human TMEFF1 is therefore a host restriction factor for HSV-1 entry into cortical neurons. Its constitutively high abundance in cortical neurons protects these cells from HSV-1 infection, whereas inherited TMEFF1 deficiency renders them susceptible to this virus and can therefore underlie HSE.

Endovascular surgical robots (ESRs) are extensively researched because of their potential to minimize surgeons’ radiation exposure. However, during the surgical operation, it is difficult for ESR to achieve the same flexibility in manipulating the soft catheter and the slender guidewire as human fingers. The flexibility of ESR can be enhanced by incorporating a soft gripper, but the nonlinear deformation of soft material presents challenges in sensing and controlling the force. To address these issues, this study proposes a deep-learning-based force sensing method that enables the flexible ESR to measure the surgical force and torque. The proposed deep-learning model consists of multiple-layer long short-term memory (LSTM) modules and is trained using the robot’s motion and soft gripper deformation datasets. The well-trained LSTM model can predict the operating force and torque in real-time. The contrast experiments with other models demonstrate that our robot exhibits higher force measurement accuracy. Furthermore, a force control strategy is proposed for the application of LSTM-based ESR. In the force control strategy, the robot is encouraged to mimic the dexterity of the surgeon’s fingers and to maintain the force within a safe range. Finally, the proposed strategy is compared with other strategies in vascular phantom experiments and is proven to effectively enhance the safety and efficiency of surgical operations.

ABSTRACTUsing 2009 to 2020 data for Chinese A-share listed firms in Shanghai and Shenzhen, we examine how much state ownership in firms is most conducive to improving corporate innovation quantity and quality. Our results are as follows: First, state ownership enhances corporate innovation by alleviating financing constraints (resource availability) but inhibits corporate innovation by weakening risk-taking (innovation willingness) and increasing agency costs (resource efficiency), leading to an inverted U-shaped relationship between state ownership and corporate innovation. Second, the percentage of state ownership that maximises innovation quality differs from that of maximising innovation quantity. State-owned enterprises (SOEs) can choose an appropriate state-ownership structure according to the actual situation in the mixed-ownership reform process. Third, non-state-owned shareholders’ control right negatively moderates the inverted U-shaped relationship between state ownership and innovation quantity but do not significantly moderate the inverted U-shaped relationship between state ownership and innovation quality. Fourth, foreign direct investment (FDI) negatively moderates the inverted U-shaped relationship between state ownership and innovation quantity and quality. Our findings reveal the impact of state ownership on corporate innovation and its transmission path, providing empirical evidence for optimising the ownership structure based on a corporate innovation perspective in the process of SOE mixed-ownership reform.

ABSTRACTThe deep-seated intersection between intrapreneurial activities and innovation presents a new paradigm with innovation at its core. This study explores the relationship between intrapreneurial capabilities and the development of high-tech small and medium-sized enterprises (HTSMEs) and examines the role and impact of ambidextrous innovation and knowledge heterogeneity. We employed a questionnaire survey of 649 HTSMEs in China, and the data are analysed using structural equation modelling. The results indicate that HTSMEs could achieve sustainable development by improving their intrapreneurial capabilities, while ambidextrous innovation plays a mediating role. When knowledge heterogeneity is introduced as a moderating variable, the results indicate that knowledge heterogeneity has a negative partial moderating effect on the relationship between intrapreneurial capabilities and ambidextrous innovation. This study contributes to our understanding of intrapreneurial capabilities by introducing balanced ambidextrous innovation and knowledge heterogeneity. Ultimately, the results suggest that HTSMEs should focus on building intrapreneurial capabilities and balance the needs of ambidextrous innovation as a path to achieving sustainable development. However, firms need to control the effect of internal knowledge heterogeneity in the innovation process.

Multimodal land use/cover classification using optical and synthetic aperture radar (SAR) images has attracted significant attention because the unique radiation and geometric characteristics of these images provide complementary information regarding land properties. However, the significant differences between these modalities create a large semantic gap, posing challenges for effective feature fusion in multimodal learning. Moreover, missing modalities often occur in practical applications due to weather constraints or sensor malfunctions, posing challenges to achieving high performance in cross-modal learning. In this study, we proposed a multimodal online knowledge distillation (MMOKD) framework, designed for land use/cover classification of optical and SAR images using either full or missing modalities. This framework trains one modality-fusion network alongside two modality-specific networks in an end-to-end manner, facilitating both multimodal and cross-modal learning. More specifically, we developed a multimodal feature fusion (MFF) module for integrating heterogeneous features and a single-modal feature generation (SFG) module for encapsulating cross-modal complementary information. In addition, we proposed the joint distillation with multitype fusion knowledge (JD-MFK) method, guiding the modality-specific student networks to comprehensively learn the modality-fusion teacher network. Notably, we adopted an online distillation strategy for real-time feedback and synchronous updates of both modality-fusion and modality-specific networks. Finally, we conducted extensive experiments on two multimodal land use/classification datasets with advanced multimodal fusion, cross-modal distillation, and specific baseline networks for comparison. The results demonstrate the effectiveness of the proposed MMODD, which not only outperforms the other networks in both full- and missing-modality scenarios but also significantly improves model training efficiency.

The cardio-cerebrovascular disease is a significant disease that can directly threaten life and health. Vascular intervention surgery is currently the mainstream treatment method. This kind of surgery requires doctors to be exposed to radiation for a long, with extensive body damage. In response, domestic and foreign researchers began to develop endovascular intervention surgery robots. Although the robot has specific additional capabilities, it is still in the research and development stage. It must continuously improve performance, reliability, and functionality before it can be used in interventional surgery. Therefore, to further reduce doctors’ learning and training costs, optimize the operation steps in surgery, and improve delivery accuracy, we developed a reciprocating delivery device-based endovascular intervention robot with multimanipulators collaboration. The third-party type test confirmed that the robot’s feasibility and overall performance meet the surgery’s needs (average linear accuracy is less than 0.1 mm). In vivo experiments have demonstrated the efficacy, safety, and reduction of radiation exposure to doctors (nearly 98.68%).

Robot-assisted vascular interventional surgery (RVIS) is an emerging technology for the treatment of vascular diseases. It has obvious advantages over traditional manual operation, such as increased accuracy, reduced fatigue, and reduced tremor. However, current research suggests that natural human–robot interaction in RVIS is still a challenge that needs to be addressed. In this article, we developed a novel robotic platform that realized magnetorheological (MR) fluids-based haptic feedback to improve the interventionist’s tactile presence. In addition, we proposed a force sensing method to accurately detect the real-time force of the flexible instrument and a collaborative operation method of the guidewire and the catheter to assist the flexible instruments in selecting the target blood vessel branch and reduce the operation difficulty of the interventionist. To verify the developed robotic platform and the proposed methods, we conducted the performance evaluation experiments in a blood vessel model and an endo vascular evaluator. The results indicated that the developed robotic platform and the proposed methods have great potential to improve the natural human–robot interaction in RVIS and guarantee safety.

In the domain of remote sensing image classification, single sensors are constrained by their sensing angles and information dimensions, rendering them incapable of fully capturing the intricate characteristics of ground objects. Different sensors can provide complementary information, significantly enhancing the performance of object classification. However, due to their unique physical observation principles and varying spatial-spectral resolutions, different modalities capture heterogeneous features of ground objects, resulting in a semantic gap issue when integrating modalities. This article designs a multimodal contrastive learning framework, starting with the preprocessing of hyperspectral image (HSI) to obtain its spatial and spectral modalities, and then combining these with the light detection and ranging (LiDAR) modality, forming a ternary modality contrastive learning framework that achieves deep semantic alignment between different modalities. Furthermore, to enhance the model’s generalization ability, based on the neighborhood semantic similarity of HSI, we propose a spectral selection data augmentation method. Extensive experiments on four public datasets show that our method outperforms several other state-of-the-art (SOTA) methods in classification performance.

The emerging ferroelectric field effect transistor (FeFET) holds tremendous potential for neuromorphic computing beyond Moore’s law, offering advantages such as high-energy efficiency, easy scalability, high-density integration, and compatibility with mainstream complementary-metal-oxide-semiconductor (CMOS) technology. In this article, we propose an analytical current–voltage ( ${I}-{V}$ ) model, which involves solving coupled Poisson, Pah-Sah, and Landau–Khalatnikov equations for long-channel double-gate FeFETs with a metal–ferroelectric–metal–insulator–semiconductor (MFMIS) structure. We rigorously validate the accuracy of our developed models through technology computer aided design (TCAD) numerical simulations and experimental data. Subsequently, we derive analytical expressions for the two critical voltages that characterize the hysteretic behavior of FeFET-based switches. Furthermore, we explore the intricate influence of voltage bias and geometrical parameters on these critical voltages, utilizing a combination of analytical expressions and SPICE simulations. The findings from this study hold significant promise in optimizing the design of FeFET-based neurons.

Mesophase-pitch-based graphite foams have attracted increasing attention because of their broad applications in thermal management, catalysis and so on. However, the common approach of using the high-pressure method for fabricating graphite foams is complex and risky. Herein, this research reports a simple and relatively safe technique for preparation of mesophase-pitch-based graphite foams, which is conducted under atmospheric pressure. Through foaming of mesophase pitch in the presence of epoxy resin at 700°C under atmospheric pressure and graphitization at 3000°C, graphite foam is fabricated. The obtained graphite foam is highly graphitic and has an open pore structure, the interlayer spacing of the (002) plane of which is about 0.3366 nm. Its density is 0.52 g/cm3with a porosity of 73.5%. The properties of graphite foams can be tailored by modifying pitch particle sizes and foaming temperatures. With an increase in pitch particle size from <25 to 75–150 μm or in foaming temperature from 400 to 850°C, the pore sizes vary from 10–25 to 35–90 μm, and their bulk densities change from 0.50 to 1.11 g/cm3. Also, a foaming mechanism is proposed wherein the residue of epoxy resin can form a framework during heating, in which the pitch softens and decomposes, forming foam. This study suggests a relatively simple and safe method for production of graphite foams from mesophase pitch.

A magnetoelectric (ME) sensor of face-shear type is demonstrated based on the ME composite consisting of (PbMg $_{{0}.{33}}$ Nb{0}.{67}O{3}}{)}_{{1}-{x}-(PbTiO $_{{3}}{)}_{x}$ piezoelectric single crystal (abbreviated as PMN-PT) and Metglas amorphous alloy. The face-shear strain is induced in PMN-PT via the magnetostriction of Metglas under a magnetic field, which generates an ME voltage through the ${d}_{{36}}$ piezoelectric coefficient. The proposed sensor reveals a high ME coupling coefficient of 48.8 V/cm Oe at resonant frequency ( $\sim $ 107.5 kHz), which is self-biased with the capability to detect ac magnetic fields of 4.58 nT at 1 kHz and 10.43 pT at resonant frequency, respectively. The self-biased face-shear mode ME sensor exhibits great potential for weak magnetic field detection.

Thermoplastic polyamide elastomer (TPAE) films were prepared by selective laser sintering technology and their organic vapor sensing behaviors were investigated. The stable responsive electrical signal was attributed to the conductive pathways evolution formed by polyetheramine soft segments. And the response behaviors of the TPAE films were evaluated using organic vapors of different polarities. Unlike conventional materials that only respond to some specific organic vapors, TPAE showed a high negative vapor coefficient (NVC) effect for all tested vapors. Rapid response, high responsivity and good repeatability were observed in continuous immersion drying run (IDR) experiments at 20 °C. The sensing behaviors during the breathing tests showed a good discrimination for different ethanol vapor concentrations. This study not only provides a guidance for the preparation of organic vapor sensors with fast response, high responsivity and stable repeatability, but also provides a strategy to broaden the application of TPAE into 4D printing field.

Achieving regioselectivity in radical cyclization reactions is of central importance, yet extremely challenging. Although Baldwin's rules provided guidance on the addition of radical species with alkenes/alkynes, the ortho-/ipso-selectivity of the cyclic reaction between radical species (especially alkyl and alkenyl radical) and aryl groups is still ambiguous. Herein, we develop an electrochemically enabled regioselective ortho-(4 + 2)/ipso-(3 + 2) cyclization of alkyl/alkenyl radicals with aryl groups, which provides a series of tetrahydronaphthalene and spirocarbocycle derivatives, exhibiting a broad substrate scope and functional group tolerance. Alkyl/alkenyl radicals are generated by Cp2Fe-mediated electrochemical oxidative radical addition of benzylic malonates with alkenes and alkynes. The method avoids the use of chemical oxidant/base/noble metal, the pre-functionalization of substrates, and the over-oxidation of compounds. Theoretical studies reveal that the dominant factor promoting the alkene-preferred ortho-addition is the favorable interaction energy; the alkyne-preferred ipso-addition regioselectivity is controlled by the distortion energy. Notably, this strategy is regarded as an important supplement to Baldwin's rules for radical cyclization.

Streptomycesspp. are well-known symbiotic microorganisms that produce antimicrobial metabolites against various pathogens. We isolated actinomycetes from the body surface of the termite Odontotermes formosanusand identified it as Streptomyces neopeptiniusBYF101 based on 16S rRNA phylogenetic analysis. Chemical analysis of the cultures of termite-associated S. neopeptiniusBYF101 via HR-MS2and GNPS analyses enabled the isolation and identification of 20 metabolites, including the unreported obscurolide-type metabolites (1–3). The chemical structures of unreported compounds (1–3) were elucidated using HR-ESI-MS and 1D and 2D NMR analysis, and their absolute configurations were determined via chemical reactions followed by the application of competing enantioselective acylation (CEA) and computational methods for ECD and DP4+ probability calculation. The isolated compounds (1–20) were tested to determine their antifungal activity against two human fungal pathogens, Candida albicansand Cryptococcus neoformans.Among the compounds tested, indole-3-carboxylic acid (9) displayed antifungal activity against C. neoformans, with an MIC value of 12 μg/mL.

Capsule robots have the potential for intestinal treatment due to their unique advantages compared with traditional diagnosis and treatment. However, current capsule robots can only carry and release one type of drug, while more drugs would be needed during the treatment. In addition, the volume of loaded drugs will be restricted due to the small dimensions of the capsule robot, and released drugs need to be guaranteed to have a sufficient dosage for treatment. In this article, to overcome these limitations, a system was proposed, and the robot consists of a leader robot module and several robot modules. Each robot module can load two types of drugs, and any two robot modules can achieve docking and separations according to the clinical requirements. Laboratory experiments were carried out to evaluate the performance. The experimental results show that the robot modules can move with speeds of 28.5, 25.1, 22.3, and 14.5 mm/s when they load drugs of 0, 0.6, 1.2, and 1.8 g, respectively. Ex vivo experiments demonstrated that two robot modules could dock and separate from each other and release drugs at target positions and did not cause damage to the lining of the intestine. This proposed multidrug modular capsule robot could prove a reference for drug release and control and have the potential for further applications.

Automatic polyp segmentation at colonoscopy plays an important role in the early diagnosis and surgery of colorectal cancer. However, the diversity of polyps in different images greatly increases the difficulty of accurately segmenting polyps. Manual segmentation of polyps in colonoscopic images is time-consuming and the rate of polyps missed remains high. In this paper, we propose a brightness prior guided attention network (BA-Net) for automatic polyp segmentation. Specifically, we first aggregate the high-level features of the last three layers of the encoder with an enhanced receptive field (ERF) module, which further fed to the decoder to obtain the initial prediction maps. Then, we introduce a brightness prior fusion (BF) module that fuses the brightness prior information into the multi-scale side-out high-level semantic features. The BF module aims to induce the network to localize salient regions, which may be potential polyps, to obtain better segmentation results. Finally, we propose a global reverse attention (GRA) module to combine the output of the BF module and the initial prediction map for obtaining long-range dependence and reverse refinement prediction results. With iterative refinement from higher-level semantics to lower-level semantics, our BA-Net can achieve more refined and accurate segmentation. Extensive experiments show that our BA-Net outperforms the state-of-the-art methods on six common polyp datasets.

4D printing technology, also known as additive manufacturing technology for smart devices, offers a wide range of promising applications in aerospace, automobile, biomedical and soft robotics. However, there are only a limited number of commercialized elastomer materials for 3D printing which needs further development, especially smart elastomers. In this work, polyamide 1212 (PA1212) based thermoplastic polyamide elastomer (TPAE) was synthesized by one-pot melt polycondensation of 1,12-dodecanedioic acid (DA), 1,12-dodecanedioic amine (DN) and polyetheramine in a stainless steel reactor and then the TPAE powder for selective laser sintering (SLS) was obtained after freeze-crushing and sieving. Subsequently, the effects of sintering temperature, laser power, layer thickness, and scan spacing during SLS processing on the properties of the sintered parts were fully investigated. The tensile strength, elastic modulus and elongation at break of the sintered parts could reach 13.7 MPa, 124 MPa and 200 %, respectively, when the sintering temperature, laser power, layer thickness and scan spacing were set as 156 °C, 21 W, 0.10 mm and 0.10 mm, respectively. Moreover, PA1212-based TPAEs fabricated by SLS showed excellent and stable shape memory behavior. This work not only provides a designing strategy of 4D printable materials and referential SLS-processing parameters, but also broadens the application of polyamide-based elastomer materials into 4D printing fields.

Tetracyanoquinodimethane (TCNQ) complexes of fluorene, dibenzofuran, dibenzothiophene, terphenyl, dithienothiophene, and carbazole show n-channel transistor properties, whereas that of diaminoterphenyl and phenothiazine exhibits ambipolar characteristics. Ambipolar transport is observed when the highest occupied molecular orbital (HOMO) level of the donor–acceptor complex is located above the ordinary hole transport limit. Since the HOMO of the charge-transfer complex is deeper than that of the component donor owing to the charge-transfer interaction, not all hole-transporting materials show hole transport in the charge-transfer complexes. In addition, the effective (super-exchange) transfer integrals have to be sufficiently large; this is frequently not satisfied when the donor HOMO is practically orthogonal to the acceptor’s lowest unoccupied molecular orbital (LUMO). By applying these criteria to calculations starting from the crystal structures, transistor properties of mixed stack charge-transfer complexes are explained.

Association Rule mining plays a crucial role in data mining by extracting relationships among the data items in the form of association rules. Fuzzy association rule mining is one of the major techniques in rule mining, and it often provides better rules and flexibility over quantitative rule mining by utilizing fuzzy set theory. The outcome of fuzzy association rule mining is greatly affected by the appropriateness of its membership functions. In this research, we have provided a comprehensive description of fuzzy membership function optimization as a Markov decision process (MDP) as well as introduced two novel algorithms- one for membership function optimization and the other for choosing the optimal number of membership functions for any given problem. This study proposes a novel multi-objective reinforcement learning technique that incorporates scalarization of different value functions while determining optimal policy and is able to construct optimized membership functions with better suitability and coverage. The proposed multi-objective reinforcement learning algorithm outperforms well-known techniques in fuzzy membership function optimization by utilizing the exploration and exploitation mechanism. The performance of the proposed techniques is clearly validated by the experimental analysis incorporated in this study and is the first study that concisely treats fuzzy membership function optimization in terms of an MDP environment it opens up enumerable opportunities for future reinforcement learning endeavors.

With the growing demand for miniaturized workspaces, the demand for microrobots has been increasing in robotics research. Compared to traditional rigid robots, soft robots have better robustness and safety. With a flexible structure, soft robots can undergo large deformations and achieve a variety of motion states. Researchers are working to design and fabricate flexible robots based on biomimetic principles, using magnetic fields for cable-free actuation. In this study, we propose an inchworm-shaped soft robot driven by a magnetic field. First, a robot is designed and fabricated and force analysis is performed. Then, factors affecting the soft robot’s motion speed are examined, including the spacing between the magnets and the strength and frequency of the magnetic field. On this basis, the motion characteristics of the robot in different shapes are explored, and its motion modes such as climbing are experimentally investigated. The results show that the motion of the robot can be controlled in a two-dimensional plane, and its movement speed can be controlled by adjusting the strength of the magnetic field and other factors. Our proposed soft robot is expected to find extensive applications in various fields.

The application of miniature underwater robots in the marine area is becoming more extensive. Occasionally, the ability of the single miniature underwater robot is limited by its size, configuration, function or propulsion method. At this moment, if a group of robots can work together in a division of labor, that can solve more problems underwater. For autonomous underwater vehicles, the support of underwater wireless communication equipment is required to achieve communication and collaboration. Acoustic communication equipment has generally been used in large underwater vehicles due to its large size and power. This article employs a small acoustic communication module to enable underwater communication in a spherical robot with a diameter of only 45 cm. The fourth-generation spherical underwater robot is exploited as the prototype. The article also verifies the accuracy, efficiency and timeliness of the proposed underwater communication systems. Finally, a cooperative mode is established based on the base station, master robot and slave robots, and some cooperation motion experiments are carried out to verify its effectiveness. Early work on this article was presented at the IEEE ICMA 2018 conference S. Gu et al., 2018. In this conference article, the authors only provided theoretical research and a small number of experiments. On this basis, this article has conducted in-depth research, proposed a base station-mater-slave robot communication mode, and established an instruction set for further experiments.

Purpose: The purpose of this paper is to examine the changes of brain functional network after electroconvulsive therapy (ECT) treatment in major depressive disorder (MDD). Design/methodology/approach: In this study, resting electroencephalography (EEG) is used to explore the changes in spectral power density, functional connectivity and network topology elicited by an acute open-label course of ECT in a group of 19 MDD subjects. The brain functional network based on Pearson correlation is constructed in a continuous threshold space (0.38–0.59). Complex network theory is used to analyze the network characteristic such as the length of the characteristic path, clustering coefficient, degree, betweenness centrality, global efficiency and small-world architecture. Findings: The results show that ECT increased the spectral power density of Delta, Theta and Alpha1 bands and the full frequency. ECT increases the functional connectivity in Delta and full frequency and reduces the functional connectivity in Alpha2 band. In the selected threshold space, the clustering coefficient, global efficiency and small-world attributes of the network are changed significantly after ECT. Originality/value: The findings indicate that resting EEG could effectively characterize the changes of brain functional networks following ECT in MDD. The results provide a theoretical basis to explore the neurophysiological mechanism of ECT in the field of MDD treatment. [ABSTRACT FROM AUTHOR]

With the advancement of technology in recent years, different new techniques are being used for classification and prediction of different complex diseases, as well as to analyse biomedical data in the medical field. Hepatitis is a liver disease that has an adverse influence on people of any age group and generally, no symptoms appear. Hence, the diagnosis of hepatitis in the early stage becomes crucial. Use of technology can ease the process and so researchers have proposed some classification techniques for early detection of hepatitis. This paper aims at summarising the up-to-the-minute techniques used for the diagnosis and prediction of hepatitis and in order to fulfil the goal, numerous articles from 1996 to 2020 have been investigated. Through this work, the mostly used algorithms and their efficiency have come to light. It is evident that support vector machine (SVM), artificial neural network (ANN) and fuzzy methods are top three techniques that have been used by the researchers and provided good performance. This research work can be helpful to develop new techniques in future by knowing the pitfalls of the previous ones.

Continuous angle estimation from surface electromyography (sEMG) is crucial for robot-assisted upper limb rehabilitation. The sEMG-based control provides an optimal way to achieve harmonic interactions between subjects and upper limb rehabilitation exoskeletons. Also, for upper limb exoskeleton systems with sEMG as the control signal, accurate identification of elbow angles from sEMG is essential. However, sEMG signals have a subject-specific nature, causing the estimation model with sEMG signals as input to have poor generalization across multiple subjects. Aiming at the above problem of intersubject variability on sEMG, multisource domain adaptation (MDA) is combined into the estimation of continuous joint movements to obtain subject-invariant features of sEMG. Also, the feature distribution of the training set and test set is evaluated using the kernel density estimation (KDE) method. Furthermore, the subject-invariant features obtained through MDA are the input of the backpropagation neural network (BPNN). Different evaluation indicators and the statistical method are used to compare the estimation results between original features and subject-invariant features, which proves the better generalization ability of the model based on subject-invariant features. Also, the estimation angle error calculated by using subject-invariant features as the input of BPNN is controlled within 10°, which shows the effectiveness of the combination of MDA and shallow neural network for the accurate subject-independent estimation of elbow joint continuous movements.

Aiming at the difficulty of communication and positioning of the existing underwater robot system, this article combines the vision system with a closed leader–follower formation structure to realize the underwater formation of three underwater robots. On account of the long-standing tracking requirements during vision-based formation process, an antiloss and redetecting strategy is fused in the proposed detecting and tracking algorithm and the binocular vision system can achieve real-time positioning underwater and calculate the 3-D coordinate so as to complete the follower's follow-up to the leader. An artificial submarine detection experiment and an antiloss redetection experiment are applied to demonstrate the robustness of the algorithm. And the underwater formation experiment is implemented so that the effectiveness of the proposed vision-based formation strategy is proved, which also provides a new solution for expanded formation system and multilevel cooperation.

The flexible grasping, force sensing, and in-hand manipulating abilities of the gripper are theoretically significant and practically valuable to solve. However, it is still difficult for most existing grippers to realize these three functions simultaneously. This article proposed a novel humanoid finger-functional parallel gripper with two actuators based on overlapping parallelogram mechanisms and the series elastic actuator (SEA). Based on the in-hand manipulation principle for the ordinary contour object (OCO), an in-hand manipulation algorithm for grasping force and position decoupling was designed. Then, to adjust the posture and position of objects within parallel fingers, the rolling and sliding conditions of objects based on the molecular-mechanical friction theory were analyzed. Combined with the proposed in-hand manipulating conditions and algorithm, the PG2 gripper can control the grasping force and manipulate the OCO in-hand controllably. Evaluation experimental results showed that the force-position control of the PG2 gripper was decoupled stably and the gripper had high accuracy in controlling force and position. Finally, the physical features perception experiments and the in-hand manipulating experiment were carried out to demonstrate the utility of the proposed manipulating methods and algorithm, and in-hand manipulating and grasping force sensing abilities of the gripper. The possible applications of this gripper include tasks that need accurate force-position control or manipulating and sensing in-hand remotely.

Robot-assisted vascular interventional surgery (VIS) changes the surgeons’ original operation, which affects their judgment and surgical safety. Therefore, the reliable safety method for robot-assisted surgery is practically valuable. However, the existing active safety methods have two challenges: the difficulty in setting inserting force thresholds suitable for the current vascular environment and the response delay caused by program processing. This article improved a series elastic actuator (SEA)-based guidewire manipulator with an adjustable grasping force function. A multifunctional finger pulp was designed to measure the guidewire inserting force and perceive the manipulating state between fingers simultaneously. Then, an orthogonal force coupling model based on the mechanical–molecular friction theory was established to demonstrate the friction mechanism of cylindrical surgical instruments grasped by elastic grasping surfaces. Based on the model, a passive safety method was proposed to adjust the grasping force autonomously, which can satisfy the inserting force requirements of the phase delivery task and control the safety threshold suitable for the current environment. The evaluation experimental results demonstrated that the designed robot system could manipulate the guidewire accurately. Then the friction experiments verified the designed orthogonal force coupling model. Finally, the in vitro experiments were performed to demonstrate that the designed passive safety method can automatically adjust the grasping force based on the inserting states of the guidewire, reduce the contact force with the vessels, and ensure surgical safety in real-time.

The recently constructed mutant libraries of diploid crops by the CRISPR-Cas9 system have provided abundant resources for functional genomics and crop breeding. However, because of the genome complexity, it is a big challenge to accomplish large-scale targeted mutagenesis in polyploid plants. Here, we demonstrate the feasibility of using a pooled CRISPR library to achieve genome-scale targeted editing in an allotetraploid crop of Brassica napus. A total of 18,414 sgRNAs were designed to target 10,480 genes of interest, and afterward, 1104 regenerated transgenic plants harboring 1088 sgRNAs were obtained. Editing interrogation results revealed that 93 of the 178 genes were identified as mutated, thus representing an editing efficiency of 52.2%. Furthermore, we have discovered that Cas9-mediated DNA cleavages tend to occur at all the target sites guided by the same individual sgRNA, a novel finding in polyploid plants. Finally, we show the strong capability of reverse genetic screening for various traits with the postgenotyped plants. Several genes, which might dominate the fatty acid profile and seed oil content and have yet to be reported, were unveiled from the forward genetic studies. Our research provides valuable resources for functional genomics, elite crop breeding, and a good reference for high-throughput targeted mutagenesis in other polyploid plants.

Stroke can cause acute damage to blood vessels in the brain, which often leads to hemiplegia and imbalances in mobility. It is a challenge to develop a motor recovery training and evaluation method with less help of therapists. In this article, a motor recovery training and evaluation method for the upper limb rehabilitation robotic system is proposed. This system has two rehabilitation units, one is active, and the other is passive. The developed rehabilitation robotic system includes an exoskeleton rehabilitation robot and PHANTOM1.5. The patients do rehabilitation training with the help of a robot in the passive unit, and a virtual reality game is designed in the active unit. Patients with mild motor impairments observe the virtual reality game interface while manipulating the PHANTOM to do rehabilitation training. Three experiments are proposed in this paper. The fuzzy neural network (FNN), spring-damper model, and the method to evaluate the training trajectory are designed and validated. The surface electromyography (sEMG) signals and grip force during rehabilitation training are collected to set up an FNN and achieve evaluation. The accuracy of the network is 0.96 which is calculated in validation set. In Section IV, the rehabilitation evaluation method is compared with the state of art on rehabilitation evaluation method. The method proposed in this article can reach high accuracy. It is easy to use and understand for patients even without the help of therapists. The problem of lacking therapists can be solved to some extent by the proposed upper limb rehabilitation system.

Exploiting functional characteristics of high-entropy boride materials is critical for extending their potential applications in harsh environments. Herein we develop high-entropy diboride (HEB2) nanoparticles with efficient catalytic performance by engineering their compositions. Based on the theoretical analysis of the lattice size difference and mixing enthalpy of HEB2and the self-propagating combustion mode of the system, six different compositional HEB2nanoparticles are synthesized viaa facile, rapid, and low-cost combustion synthesis method. By engineering their compositions, we achieve the efficient catalytic ability of HEB2nanoparticles in persulfate activation towards antibiotics removal, namely the highest tetracycline removal efficiency of 93.5%. Further investigations of the adsorption energy and the charge density difference of various adsorption sites viadensity functional theory confirm that the outstanding catalytic ability mainly originates from the Ti constituent rather than the high-entropy effect. In addition, a potential catalytic mechanism of HEB2towards persulfate activation for antibiotics degradation is proposed based on the detected reactive oxygen species and the computational reaction energy barrier. This study not only provides theoretical basis for their component design in the efficient catalytic degradation of antibiotics, but also shows a promising application of HEB2for removal of emerging contaminants from environmental water matrices.

Bilateral rehabilitation systems with bilateral or unilateral assistive robots have been developed for hemiplegia patients to recover their one-side paralysis. However, the compliant robotic assistance to promote bilateral inter-limb coordination remains a challenge that should be addressed. In this paper, a biomimetic variable stiffness modulation strategy for the Variable Stiffness Actuator (VSA) integrated robotic is proposed to improve bilateral limb coordination and promote bilateral motor skills relearning. An Electromyography (EMG)-driven synergy reference stiffness estimation model of the upper limb elbow joint is developed to reproduce the muscle synergy effect on the affected side limb by independent real-time stiffness control. Additionally, the bilateral impedance control is incorporated for realizing compliant patient–robot interaction. Preliminary experiments were carried out to evaluate the tracking performance and investigate the multiple task intensities’ influence on bilateral motor skills relearning. Experimental results evidence the proposed method could enable bilateral motor task skills relearning with wide-range task intensities and further promote bilateral inter-limb coordination.

Bilateral rehabilitation robotics can allow hemiplegia patients to regain the cooperative capabilities of both arms by synchronized coordination movements. Furthermore, the variable stiffness actuators (VSA) integrated robotics can offer compliant advantages for human-robot interaction. Although various studies have proposed to improve training safety and comfortability by VSA, few studies have focused on inducing patient active participation by VSA-based variable stiffness control for bilateral rehabilitation. In this article, an surface electromyography (sEMG) driven variable stiffness control framework with a novel training task quantitative factor TPI was proposed to promote patient active participation in upper limb bilateral rehabilitation. The proposed control law integrates an sEMG-driven musculoskeletal model for providing real-time dynamic reference stiffness from the nonparetic limb as a task skill learning guide to the affected limb. Furthermore, the proposed TPI is designed in the high-level controller for rendering smooth and automatic transition among three patient-robot interaction modes for inducing active participants. In the low-level controller, a position-based bilateral impedance control and a cascaded backstepping position control were implemented for compliant task position planning and tracking. Preliminary experimental results show that the proposed method can promote patient active participation by providing minimal intervention assistance for facilitating efficient upper limb rehabilitation.

This article presents a visualization method for defects using the alternating current field measurement (ACFM) technique. Only a single-axis tunneling magneto-resistive (TMR) sensor is used to collect the ${B}_{x}$ signal, which can determine the length and width, and indicate the depth of the defect simultaneously. A simulation is conducted to explore the changing rule of the ${B}_{x}$ signal with the dimensions of the defect. The simulated ${B}_{x}$ signals are processed by taking partial differentials with respect to the ${x}$ - and ${y}$ -axes. It is found that the distances between peaks and troughs of partial differentials in both directions ( ${l}_{x}$ and ${l}_{y}{)}$ can represent the defect length and width, respectively. A magnetic imaging algorithm is proposed to calculate the morphology of the defects based on simulation results. An aluminum work-piece with two types of defects is tested to validate the proposed imaging method using an elaborate ACFM detection system. Experimental results show that the defects can be characterized visually and accurately.

During marine missions, AUVs are susceptible to external disturbances, such as obstacles, ocean currents, etc., which can easily cause mission failure or disconnection. In this paper, considering the strong nonlinearities, external disturbances and obstacles, the kinematic and dynamic model of bioinspired Spherical Underwater Robot (SUR) was described. Subsequently, the waypoints-based trajectory tracking with obstacles and uncertainties was proposed for SUR to guarantee its safety and stability. Next, the Lyapunov theory was adopted to verify the stability and the Slide Mode Control (SMC) method is used to verify the robustness of the control system. In addition, a series of simulations were conducted to evaluate the effectiveness of proposed control strategy. Some tests, including path-following, static and moving obstacle avoidance were performed which verified the feasibility, robustness and effectiveness of the designed control scheme. Finally, a series of experiments in real environment were performed to verify the performance of the control strategy. The simulation and experimental results of the study supplied clues to the improvement of the path following capability and multi-obstacle avoidance of AUVs.

With the development of camera technology, high-speed cameras have greatly contributed to capturing the movement and posture of animals, which has dramatically promoted experimental biology research. At the same time, with the concept of bionics gradually gaining popularity among researchers, the design of robots is absorbing more and more biological features, where the interest in the bio-inspired robot is hewed out. Compared with the traditional robot, the bio-inspired robot imitates the motion pattern to achieve similar propulsion features, which may be more effective and reasonable. In this paper, the motion patterns of aquatic animals are divided into four categories according to their propulsion mechanisms: drag-based, lift-based, jet-based, and interface-based. And bio-inspired robots imitating aquatic prototypes are introduced and reviewed. Finally, the prospect of aquatic bio-inspired robots is discussed.

Surface electromyography (sEMG) has great application potential in upper extremity rehabilitation exoskeleton. The accurate identification of elbow motion angle is crucial for the sEMG-controlled upper limb exoskeleton rehabilitation system. However, the existing high intersubject variability in sEMG limits the generality of the model built through learning algorithms among different subjects. Aiming at the above problem, a feature selection method based on a two-stage genetic algorithm (GA) is proposed for the accurate user-independent estimation of continuous movements. And the information theory-based minimum redundancy maximum relevance criterion serves as the fitness function to evaluate the goodness of subsets. The effectiveness of the proposed method is verified by estimating the motion angle of the elbow joint using the collected sEMG data of six participants. The prediction performance is compared with that before the two-stage GA-based feature selection (TS-GAFS), and different metrics and statistical analyses are adopted to evaluate the results. The estimation angle error calculated after TS-GAFS is controlled within 10°, which shows the feasibility of the proposed method for the accurate user-independent estimation of continuous joint movements.

• An efficient approach is proposed to prepare ECM coatings without damaging cells. • The detached cells are still active and can be reused to generate new ECM coatings. • The obtained ECM coatings maintain the features of the natural ECM. • The ECM coatings promote cell adhesion and guide the stem cell differentiation. • This method can extract pure ECM coatings without cell residual and fragments. Extracellular matrix (ECM) provides a variety of physical and chemical cues for cells. Here, a very simple and smart method is developed to glue living cells away for harvesting their ECMs. The obtained ECM coatings show less cell fragment residues comparing with those obtained by the traditional cell lysis. The glued cell sheets can even be re-cultured and reused after transferring to new environment. This moderate way well maintains the activity of the ECM proteins, which can promote cell adhesion and growth. Strikingly, the ECM coatings acquired from different functional cells can guide stem cell differentiation, which is attributed to the natural physical and biochemical cues on ECM coatings. Consequently, this method provides a substantial progress for preparing natural ECM coatings and shows promising potential in regenerative medicine and other related fields of biomedical engineering. [Display omitted] [ABSTRACT FROM AUTHOR]