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This paper proposed a skill transmission technique for the mobile robot via learning by demonstration. When the material is transported to the designated location, the robot can show the human-like capabilities: autonomous tracking target. In this case, a skill transmission framework is designed, which the Kinect sensor is utilized to distinguish human activity recognition to create a planned path. Moreover, the dynamic movement primitive method is implemented to represent the teaching data, and the Gaussian mixture regression is utilized to encode the learning trajectory. Furthermore, in order to realize the accurate position control of trajectory tracking, a model predictive tracking control is investigated, where the recurrent neural network is used to eliminate the uncertain interaction. Finally, some experimental tasks using the mobile robot (BIT-6NAZA) are carried out to demonstrate the effectiveness of the developed techniques in real-world scenarios.

Manipulation skill learning and generalisation have gained increasing attention due to the wide applications of robot manipulators and the spurt of robot learning techniques. Especially, the learning from demonstration method has been exploited widely and successfully in the robotic community, and it is regarded as a promising direction to realise the manipulation skill learning and generalisation. In addition to the learning techniques, the immersive teleoperation enables the human to operate a remote robot with an intuitive interface and achieve the telepresence. Thus, it is a promising way to transfer manipulation skills from humans to robots by combining the learning methods and teleoperation, and adapting the learned skills to different tasks in new situations. This review, therefore, aims to provide an overview of immersive teleoperation for skill learning and generalisation to deal with complex manipulation tasks. To this end, the key technologies, for example, manipulation skill learning, multimodal interfacing for teleoperation and telerobotic control, are introduced. Then, an overview is given in terms of the most important applications of immersive teleoperation platform for robot skill learning. Finally, this survey discusses the remaining open challenges and promising research topics.

Objective To investigate the lung cancer-promoting mechanism of mesenchymal stem cell-secreted extracellular vesicles (MSC-EV). Methods EV were isolated from culture media of human bone marrow-derived MSCs that were pre-challenged with or without hypoxia (referred to as H-EV and N-EV, respectively). After treatment with N-EV or H-EV, A549 and H23 cell proliferation, apoptosis, trans-well invasion and epithelial-to-mesenchymal transition (EMT) were examined. Polarization of human primary monocytes-derived macrophages with or without N-EV or H-EV induction were analyzed by flow cytometry and ELISA. PTEN, PDCD4 or RECK gene was overexpressed in A549 cells, while miR-21-5p was knocked down in MSCs, A549 or H23 lung cancer cells or primary monocytes by miR-21-5p inhibitor transfection. Protein level of PTEN, PDCD4, RECK, AKT or STAT3 as well as phosphorylation level of AKT or STAT3 protein were assayed by western blot. Tumorigenicity of A549 and H23 cells with or without MSC-EV co-injection was assayed on immunocompromised mice. The xenograft tumor were examined for cell proliferation, angiogenesis, apoptosis and intra-tumoral M1/M2 macrophage polarization. Results Comparing to N-EV, H-EV treatment significantly increased A549 and H23 cell proliferation, survival, invasiveness and EMT as well as macrophage M2 polarization. MiR-21-5p knocked down significantly abrogated the cancer-promoting and macrophage M2 polarizing effects of H-EV treatment. H-EV treatment downregulated PTEN, PDCD4 and RECK gene expression largely through miR-21-5p. Overexpressing PTEN, PDCD4 and RECK in A549 cells significantly reduced the miR-21-5p-mediated anti-apoptotic and pro-metastatic effect of H-EV, while overexpressing PTEN in monocytes significantly reduced macrophage M2 polarization after induction with the presence of H-EV. H-EV co-injection significantly increased tumor growth, cancer cell proliferation, intra-tumoral angiogenesis and M2 polarization of macrophages in vivo partially through miR-21-5p. Conclusions Increased miR-21-5p delivery by MSC-EV after hypoxia pre-challenge can promote lung cancer development by reducing apoptosis and promoting macrophage M2 polarization.

Background The triglyceride–glucose (TyG) index, which is a reliable surrogate marker of insulin resistance (IR), has been associated with cardiovascular diseases. However, evidence of the impact of the TyG index on the severity of coronary artery disease (CAD) is limited. This study investigated the relationship between the TyG index and CAD severity of individuals with different glucose metabolic statuses. Methods This study enrolled 2792 participants with CAD in China between January 1, 2018 and December 31, 2021. All participants were divided into groups according to the tertiles of the TyG index as follows: T1 group, TyG index < 6.87; T2 group, TyG index ≥ 6.87 to < 7.38; and T3 group, TyG index ≥ 7.38. The glucose metabolic status was classified as normal glucose regulation, pre-diabetes mellitus (pre-DM), and diabetes mellitus according to the standards of the American Diabetes Association. CAD severity was determined by the number of stenotic vessels (single-vessel CAD versus multi-vessel CAD). Results We observed a significant relationship between the TyG index and incidence of multi-vessel CAD. After adjusting for sex, age, body mass index, smoking habits, alcohol consumption, hypertension, estimated glomerular filtration rate, antiplatelet drug use, antilipidemic drug use, and antihypertensive drug use in the logistic regression model, the TyG index was still an independent risk factor for multi-vessel CAD. Additionally, the highest tertile of the TyG group (T3 group) was correlated with a 1.496-fold risk of multi-vessel CAD compared with the lowest tertile of the TyG group (T1 group) (odds ratio [OR], 1.496; 95% confidence interval [CI], 1.183–1.893; P < 0.001) in the multivariable logistic regression model. Furthermore, a dose–response relationship was observed between the TyG index and CAD severity (non-linear P = 0.314). In the subgroup analysis of different glucose metabolic statuses, the T3 group (OR, 1.541; 95% CI 1.013–2.344; P = 0.043) were associated with a significantly higher risk of multi-vessel CAD in individuals with pre-DM. Conclusions An increased TyG index was associated with a higher risk of multi-vessel CAD. Our study indicated that TyG as an estimation index for evaluating IR could be a valuable predictor of CAD severity, especially for individuals with pre-DM.

As a significant role in healthcare and sports applications, human activity recognition (HAR) techniques are capable of monitoring humans’ daily behavior. It has spurred the demand for intelligent sensors and has been giving rise to the explosive growth of wearable and mobile devices. They provide the most availability of human activity data (big data). Powerful algorithms are required to analyze these heterogeneous and high-dimension streaming data efficiently. This paper proposes a novel fast and robust deep convolutional neural network structure (FR-DCNN) for human activity recognition (HAR) using a smartphone. It enhances the effectiveness and extends the information of the collected raw data from the inertial measurement unit (IMU) sensors by integrating a series of signal processing algorithms and a signal selection module. It enables a fast computational method for building the DCNN classifier by adding a data compression module. Experimental results on the sampled 12 complex activities dataset show that the proposed FR-DCNN model is the best method for fast computation and high accuracy recognition. The FR-DCNN model only needs 0.0029 s to predict activity in an online way with 95.27% accuracy. Meanwhile, it only takes 88 s (average) to establish the DCNN classifier on the compressed dataset with less precision loss 94.18%.

Robotics have advanced significantly over the years, and human–robot interaction (HRI) is now playing an important role in delivering the best user experience, cutting down on laborious tasks, and raising public acceptance of robots. New HRI approaches are necessary to promote the evolution of robots, with a more natural and flexible interaction manner clearly the most crucial. As a newly emerging approach to HRI, multimodal HRI is a method for individuals to communicate with a robot using various modalities, including voice, image, text, eye movement, and touch, as well as bio-signals like EEG and ECG. It is a broad field closely related to cognitive science, ergonomics, multimedia technology, and virtual reality, with numerous applications springing up each year. However, little research has been done to summarize the current development and future trend of HRI. To this end, this paper systematically reviews the state of the art of multimodal HRI on its applications by summing up the latest research articles relevant to this field. Moreover, the research development in terms of the input signal and the output signal is also covered in this manuscript.

Hepatocellular carcinoma (HCC) is a primary liver malignancy characterized by a high mortality rate and unfavorable prognosis. Altered epigenetic modifications have been closely associated with cancer development and tumor immune escape. RNA methylation is a pervasive epigenetic alteration. N6-methyladenosine (m6A), 5-methylcytosine (m5C), N1-methyladenosine (m1A), N7-methylguanosine (m7G), 3-methylcytidine (m3C), pseudouridine (Ψ), and 2′-O-methylation (Nm) are the main types of RNA methylation. Importantly, abnormal RNA modifications in HCC are key drivers in promoting the translation of oncogenic RNA transcripts. This not only provides cancer cells with a growth-promoting edge but also significantly contributes to tumorigenesis, fueling processes such as uncontrolled cell proliferation, invasion, and metastasis. RNA methylation influences metabolic reprogramming, immune cells, and immunological factors by modulating biological processes like RNA splicing, translation, stability, and translocation. Consequently, RNA methylation is pivotal in modulating biological processes including HCC tumor immunity, proliferation, invasion, and metastasis. This paper systematically examines the mechanisms and functions of these seven types of RNA methylations, offering a thorough overview of their roles and probable mechanisms within the HCC tumor microenvironment and immune system. We seek to offer novel insights and ways to enhance the effectiveness of HCC immunotherapy. Graphical abstract

The control of variable stiffness actuators (VSAs) is challenging because they have highly nonlinear characteristics and are difficult to model accurately. Classical control approaches using high control gains can make VSAs stiff, which alters the inherent compliance of VSAs. Iterative learning control can achieve high tracking accuracy for VSAs but generally lacks sufficient generalization. This study applies Gaussian process (GP) regression to design a stable tracking controller combining feedforward and low-gain feedback control actions for agonistic-antagonistic (AA) VSAs subjected to unknown dynamics. The GP model learns the inverse dynamics of AA-VSAs and provides model fidelity by the predicted variance. The stability analysis of the closed-loop system demonstrates that the tracking error is uniformly ultimately bounded and exponentially converges to a small ball under a given probability. Experiments on an AA-VSA named qbmove Advanced have validated the superiority of the proposed method with respect to tracking accuracy and generalization.

HighlightsInspired by the Chinese Knotting weave structure, an electromagnetic interference (EMI) nanofiber composite membrane with a twill surface was prepared.The EMI shielding efficiency (SE) of the composite membrane was 103.9 dB when the MXene/silver nanowires (MXene/AgNW) content was only 7.4 wt% and the surface twill structure improved the EMI by 38.5%.The nanofiber composite membrane demonstrated an excellent thermal management performance, hydrophobicity, non-flammability, and performance stability, which was demonstrated by an EMI SE of 97.3% in a high-temperature environment at 140 °C.Inspired by the Chinese Knotting weave structure, an electromagnetic interference (EMI) nanofiber composite membrane with a twill surface was prepared. Poly(vinyl alcohol-co-ethylene) (Pva-co-PE) nanofibers and twill nylon fabric were used as the matrix and filter templates, respectively. A Pva-co-PE-MXene/silver nanowire (Pva-co-PE-MXene/AgNW, PMxAg) membrane was successfully prepared using a template method. When the MXene/AgNW content was only 7.4 wt% (PM7.4Ag), the EMI shielding efficiency (SE) of the composite membrane with the oblique twill structure on the surface was 103.9 dB and the surface twill structure improved the EMI by 38.5%. This result was attributed to the pre-interference of the oblique twill structure in the direction of the incident EM wave, which enhanced the probability of the electromagnetic waves randomly colliding with the MXene nanosheets. Simultaneously, the internal reflection and ohmic and resonance losses were enhanced. The PM7.4Ag membrane with the twill structure exhibited both an outstanding tensile strength of 22.8 MPa and EMI SE/t of 3925.2 dB cm−1. Moreover, the PMxAg nanocomposite membranes demonstrated an excellent thermal management performance, hydrophobicity, non-flammability, and performance stability, which was demonstrated by an EMI SE of 97.3% in a high-temperature environment of 140 °C. The successful preparation of surface-twill composite membranes makes it difficult to achieve both a low filler content and a high EMI SE in electromagnetic shielding materials. This strategy provides a new approach for preparing thin membranes with excellent EMI properties.