Curve running is a common form of training and competition. Conducting research on posture estimation during curve running can provide more accurate training and competition data for athletes. However, due to the unique nature of curve running, traditional posture estimation methods neglect the temporal changes in athlete posture, resulting in a decrease in estimation accuracy. Therefore, a posture estimation method for curve running motion using nano-biosensor and machine learning is proposed. First, the motion parameters of humans are collected by nano-biosensor, and the posture coordinates are obtained preliminarily. Second, the posture coordinates are established according to the human motion parameters, and the curve running posture data is obtained and filtered to obtain more accurate data. Finally, the Bayesian network in machine learning is used to continuously track the posture, and a nonlinear equation is established to fuse the posture angle obtained by the sensor and the posture tracked by the Bayesian network, to realize the posture estimation of curve running motion. The results show that the proposed estimation method has a good motion posture estimation effect, and the hip joint estimation error, knee joint estimation error and ankle joint estimation error are all less than 5°, and the endpoint displacement estimation offset rate is less than 2%. It can realize accurate motion posture estimation of curve running motion, and has important application value in the field of track training.
Hydrogels possess exceptional physical and chemical properties that render them appealing components for soft actuators, wearable technologies, healthcare devices, and human interactive robots. Especially, the stimuli-responsive hydrogels can sense and perform smart functions in the presence of various stimuli that collectively contribute to the intelligence of the soft machine systems. Furthermore, facile modification of hydrogels with other functional groups/additives/nanofillers substantially expands their functionalities and further broadens the scope of their application. Designing suitable hydrogels with adequate capabilities and engineering effective configurations are of supreme importance for the development of advanced hydrogel-based soft machines. Herein, this review summarizes recent advances of stimuli-responsive hydrogels in multifunctional soft machines, such as robotics, actuators, and sensors. Functions including multistimuli responsiveness, self-healing, and high biocompatibility can be endowed to the soft machines through designing advanced hydrogel materials, which would not be possible with an approach based on conventional elastic materials (e.g. rubbers, elastomers). To close, future opportunities and challenges this field faces are emphasized and discussed for the development of exciting new hydrogel-based devices in real-world conditions.
The paper concerns a fractional Schrödinger–Poisson system involving Hardy potentials. By using Nehari manifold method, we prove the existence and asymptotic behaviour of ground state solution.
Scanning probe microscopy (SPM) is recognized as an essential characterization tool in a broad range of applications, allowing for real-space atomic imaging of solid surfaces, nanomaterials, and molecular systems. Recently, the imaging of chiral molecular nanostructures via SPM has become a matter of increased scientific and technological interest due to their imminent use as functional platforms in a wide scope of applications, including nonlinear chiroptics, enantioselective catalysis, and enantiospecific sensing. Due to the time-consuming and error-prone image analysis process, a highly efficient analytic framework capable of identifying complex chiral patterns in SPM images is needed. Here, we adopted a state-of-the-art machine vision algorithm to develop a one-image-one-system deep learning framework for the analysis of SPM images. To demonstrate its accuracy and versatility, we employed it to determine the chirality of the molecules comprising two supramolecular self-assemblies with two distinct chiral organization patterns. Our framework accurately detected the position and labeled the chirality of each molecule. This framework underpins the tremendous potential of machine learning algorithms for the automated recognition of complex SPM image patterns in a wide range of research disciplines.
Two-dimensional MXene materials have demonstrated attractive electrical and electrochemical properties in energy storage applications. Adding stretchability to MXene remains challenging due to its high mechanical stiffness and weak intersheet interaction, so the assembling techniques for mechanically stable MXene architectures require further development. We report a simple fabrication by harnessing the interfacial instability to generate higher dimensional MXene nanocoatings capable of programmed crumpling/unfolding. A sequential patterning approach enabled the design of sequence-dependent MXene textures across multiple length scales, which were utilized for controllable wetting surfaces and high-areal-capacitance electrodes. We next transferred the crumpled MXene nanocoating onto an elastomer to fabricate an MXene/elastomer electrode with high stretchability. The accordion-like MXene can be reversibly folded/unfolded and still preserve efficient specific capacitances. We further fabricated asymmetric MXene supercapacitors, and the devices demonstrated efficient electrochemical performance and large deformability (180° bendability, 100% stretchability). Our texturing techniques can be applied to large MXene families for designing stretchable architectures in wearable electronics.
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