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As unmanned aerial vehicles (UAVs) are widely used in various fields, there is an increasing demand for UAV anti-jamming, multipath mitigation, and covert secrecy. Frequency-hopping binary offset carrier (FH-BOC) signals possess higher anti-jamming and multipath mitigation capabilities than direct-sequence spread spectrum (DSSS) and binary offset carrier (BOC) signals. A prerequisite for constructing communication links between UAVs using FH-BOC signals is the design of efficient acquisition algorithms to capture the signals successfully. In this paper, the modulation and characteristics of the FH-BOC signal are introduced. The maximum relative velocity between UAVs is 5.5 km/s, the maximum acceleration is 50 g, and the maximum plus acceleration is 20 g/s. In this high dynamic environment, the parameters for the parallel code phase and Partial Matched Filter–Fast Fourier Transform (PMF-FFT) acquisition algorithms targeting FH-BOC(10,1) signals are designed, and the acquisition performance of these algorithms is comparatively analyzed. The acquisition time for the first and second algorithms is 4.3317 s and 6.137 s. The number of real additions required by the first and second algorithms is approximately 10.9×109 and 8.9×109, and the number of real multiplications is approximately 7.6×109 and 6.7×109. This helps in selecting the acquisition algorithm when FH-BOC signals are used to build inter-UAV communication links.

In the process of wireless energy transmission from a Space Solar Power Station (SSPS) to a satellite, the efficiency of energy transmission is closely related to the accuracy of beam control. The existing methods commonly ignore the impact of array position, structural deviation of the transmitting antenna, and modulation errors, which leads to the deviation error in actual energy transmission beams and the reduction of energy transmission efficiency. This paper innovatively proposes a high-precision bi-directional beam-pointing measurement method, which provides a technical basis for advancing the beam-pointing control accuracy from the perspective of improving the beam-pointing measurement accuracy. The method consists of (1) the interferometer goniometry method to realize high-precision guiding beam pointing measurement; and (2) the power field reconstruction method to realize offset angle measurement of the energy-transmitting beam. Simulation results demonstrate that under dynamic conditions, the guiding beam-pointing measurement accuracy of this method reaches 0.05°, which is better than the traditional 0.1° measurement accuracy based on the guiding beam. The measurement accuracy of the offset distance of the energy center is better than 0.11 m, and the measurement accuracy of the offset angle is better than 0.012°.

Inspired by “crimped” collagen fiber, which induce a low-level load region within the hierarchical structures of ligaments and tendons, this study introduces a braiding technology aiming at characterizing nonlinear mechanical behavior. Samples of the developed artificial ligament with a multilayer braided structure were fabricated and tested. Alterations in the number of braiding layers, wales, and courses significantly affect the mechanical properties of the artificial ligaments. Specifically, the toe length exhibited an increase of up to 104.58 %, while the linear stiffness exhibited variations concomitant with changes in these parameters. Additionally, a mathematical model to describe the relationship between the toe length and linear stiffness was developed based on the braiding parameters and material properties of the fibers. Alternative fibers were employed and tested to demonstrate a consistency with the mathematical model. The correlation coefficients were calculated as 0.9644 for the toe length and 0.9203 for the linear stiffness. Lastly, this study mimics the mechanical behavior of the human medial collateral ligament (MCL) by integrating the artificial ligament into a bio-fidelity knee model with a joint stiffness of 1.77 Nm/deg, resulting in a high degree of similarity with human knee in valgus-varus, further affirming the utility and applicability of this study.

BackgroundWhile acupuncture treatment has gained extensive usage in addressing headaches, there remains a notable gap in the literature analysis for this field. Therefore, this study aims to conduct a literature review using Citespace, VOSviewer, and Bibliometrix, aiming to examine the current status, strengths, and potential future directions in the utilization of acupuncture for headache treatment.MethodsRelevant literature on acupuncture treatment for headaches between 2003 and 2023 was retrieved from the Web of Science (WoS) core database. Utilizing CiteSpace 6.1.R6, VOSviewer 1.6.18, and Bibliometrix 4.1.4, we conducted bibliometric analyses across various categories, including countries/regions, institutions, authors, journals, references, and keywords.ResultsA total of 808 research reports were included. China and the United States have significantly contributed to this field. Chengdu University of Chinese Medicine holds the record for the highest number of published papers. Liu Lu has the highest publication output, while Linde K has the highest citation rate. MEDICINE leads in publication frequency, while CEPHALALGIA holds the highest citation rate. The Long-term Effect of Acupuncture for Migraine Prophylaxis a Randomized Clinical Trial is the most cited reference. Migraine was the most researched type. Filiform needle acupuncture was the most widely used stimulation method. The safety and efficacy of acupuncture have received significant attention. Modern mechanism research shows that depression, brain functional connectivity, and neuroimaging technology have become research hotspots in the acupuncture treatment of headaches.ConclusionAcupuncture treatment for headaches has established a stable trend with a promising developmental trajectory. Research in this field mainly focuses on different acupuncture prevention and treatment for various types of headaches, the safety and efficacy of acupuncture, etc. Research on the mechanism of action mainly focuses on interpreting bidirectional and holistic regulation between pain and emotion by acupuncture and the regulation of brain function connection and neuroimaging technology by acupuncture. Future research should expand on the advantages and indications of acupuncture treatment for different headaches and their modern mechanisms.

Background and objective: Tricuspid regurgitation (TR) is one of the most common forms of valvular heart diseases. The morphological information of the tricuspid valve annulus (TVA) is critical in treatment planning for TR. It is necessary to extract the TVA from medical images to obtain that information, however this task is difficult and time-consuming to perform manually. In this paper, we propose a method to automatically extract and measure the TVA from computed tomography (CT) images. Methods: Our proposed method coarsely crops CT images to the region surrounding the tricuspid valve based on the right atrium and the right ventricle regions. The cropped CT images are input to a stacked hourglass network with loss function integrating the mean squared error loss, the focal loss and the shape-aware weighted Hausdorff distance loss to extract 36 landmarks on the TVA. The extraction accuracy of TVA landmarks was evaluated by five-fold cross validation using 120 CT images with manually annotated TVA landmarks. In addition, measurements of TVA morphology based on automatically extracted TVA and those based on manually annotated TVA were calculated and compared using the same measurement algorithm which provides a means to automatically generate seven measurements based on TVA landmarks. Results: Our proposed method extracted TVA inside the right heart in all CT images without any processing interruption. The mean processing time was 27.09 ± 8.65 s, and the Chamfer distance and Hausdorff distance were 2.07 ± 0.53 and 4.09 ± 1.29, respectively. The mean absolute error between the measurements based on automatically extracted TVA and those based on manually annotated TVA was less than 4 mm, which is less than the typical device size interval for surgical prosthetic valve rings in current use, for measurement items related to distance. For all seven measurement items, significant correlations (r = 0.51–0.99, p

Similar to biological muscles in nature, artificial muscles have unique advantages for driving bionic robots. However, there is still a large gap between the performance of existing artificial muscles and biological muscles. Twisted polymer actuators (TPAs) convert rotary motion from torsional to linear motion. TPAs are known for their high energy efficiency and large linear strain and stress outputs. A simple, lightweight, low-cost, self-sensing robot powered using a TPA and cooled using a thermoelectric cooler (TEC) was proposed in this study. Because TPA burns easily at high temperatures, traditional soft robots driven by TPAs have low movement frequencies. In this study, a temperature sensor and TEC were combined to develop a closed-loop temperature control system to ensure that the internal temperature of the robot was 5 °C to cool the TPAs quickly. The robot could move at a frequency of 1 Hz. Moreover, a self-sensing soft robot was proposed based on the TPA contraction length and resistance. When the motion frequency was 0.01 Hz, the TPA had good self-sensing ability and the root-mean-square error of the angle of the soft robot was less than 3.89% of the measurement amplitude. This study not only proposed a new cooling method for improving the motion frequency of soft robots but also verified the autokinetic performance of the TPAs.

Plant GDP-D-mannose pyrophosphorylase (GMPase) catalyzes a committed step in ascorbic acid biosynthesis pathway. Arabidopsis thaliana VTC1 is the first genetically characterized plant GMPase and has unique properties when compared with bacterial and animal homologs. Here we present the crystal structures of VTC1 in the unliganded and product-bound states at resolutions of 2.8 and 3.0 Å, respectively. VTC1 dimerizes in a same way like other known GMPases, but dodecamerizes in a previously unobserved arrangement. The interactions to GDP-D-mannose and inorganic pyrophosphate are revealed by the product-bound VTC1 structure. An in vitro GMPase activity assay confirms the regulatory role of the C-terminal left-handed β-helix domain, and structural analyses suggest the models of VTC1 hetero-complex with its interacting proteins. The structural information advances our insights into the different mechanisms involved in VTC1 regulation.

Bimetallic catalysts (Ni-Co/AC and Ni-Fe/AC) supported on activated carbon (AC) were prepared via one-step method from coal as AC precursor with the addition of metal salts by KOH activation. The effects of the introduction of second metal (Co or Fe) into Ni/AC on the textural structure of the resultant bimetallic catalysts and their catalytic performances for methane decomposition were investigated. The results showed that active metals can be directly supported on AC by the reaction of metal species with carbon during the activation. The addition of Co or Fe to Ni/AC resulted in the decrease of specific surface area and pore volume. With increasing the loading of Co or Fe, metal alloys were formed and total surface area and pore volume declined, but the mesoporosity was increased. Bimetallic Ni-Co/AC and Ni-Fe/AC catalysts exhibited better catalytic activity and stability for methane decomposition compared than Ni/AC. The introduction of Co mainly improved initial catalytic activity; however, Ni-Fe/AC catalyst showed better behaviors in terms of reducing the deactivation rate of Ni-based catalyst than Ni-Co/AC catalyst, which is relative with the formation of Ni-Fe alloy and carbon fibers over Ni-Fe/AC. This work provides a simple and efficient approach to improve catalytic performances of Ni-based catalyst for methane decomposition.

The human tibiofibular complex has undergone a long evolutionary process, giving its structure a high bearing-capacity. The distinct tibiofibular shape can be used in engineering to acquire excellent mechanical properties. In this paper, four types of bionic tubes were designed by extracting the dimensions of different cross-sections of human tibia–fibula. They had the same outer profiles, but different inner shapes. The concept of specific stiffness was introduced to evaluate the mechanical properties of the four tubes. Finite-element simulations and physical bending-tests using a universal testing machine were conducted, to compare their mechanical properties. The simulations showed that the type 2 bionic tube, i.e., the one closest to the human counterpart, obtained the largest specific-stiffness (ε = 6.46 × 104), followed by the type 4 (ε = 6.40 × 104) and the type 1 (ε = 6.39 × 104). The type 3 had the largest mass but the least stiffness (ε = 6.07 × 104). The specific stiffness of the type 2 bionic tube increased by approximately 25.8%, compared with that of the type 3. The physical tests depicted similar findings. This demonstrates that the bionic tube inspired by the human tibiofibular shape has excellent effectiveness and bending properties, and could be used in the fields of healthcare engineering, such as robotics and prosthetics.

Muscles are capable of modulating the body and adapting to environmental changes with a highly integrated sensing and actuation. Inspired by biological muscles, coiled/twisted fibers are adopted that can convert volume expansion into axial contraction and offer the advantages of flexibility and light weight. However, the sensing-actuation integrated fish line/yarn-based artificial muscles are still barely reported due to the poor actuation-sensing interface with off-the-shelf fibers. We report herein artificial coiled yarn muscles with self-sensing and actuation functions using the commercially available yarns. Via a two-step process, the artificial coiled yarn muscles are proved to obtain enhanced electrical conductivity and durability, which facilitates the long-term application in human-robot interfaces. The resistivity is successfully reduced from 172.39 Ω·cm (first step) to 1.27 Ω·cm (second step). The multimode sense of stretch strain, pressure, and actuation-sensing are analyzed and proved to have good linearity, stability and durability. The muscles could achieve a sensitivity (gauge factor, GF) of the contraction strain perception up to 1.5. We further demonstrate this self-aware artificial coiled yarn muscles could empower non-active objects with actuation and real-time monitoring capabilities without causing damage to the objects. Overall, this work provides a facile and versatile tool in improving the actuation-sensing performances of the artificial coiled yarn muscles and has the potential in building smart and interactive soft actuation systems.

Updated concrete construction robots are designed to optimize equipment operation, improve safety, enhance workspace awareness, and further ensure a proper working environment for construction workers. The importance of concrete construction robots has been constantly highlighted, as they have a profound impact on construction quality and efficiency. Autonomous vehicle driving monitoring has been widely employed in concrete construction robots; however, they lack clear relevance to the key functions in the building process. This paper aims to bridge this knowledge gap by systematically classifying and summarizing the existing concrete construction robots, analyzing their existing problems, and providing direction for their future development. The prescription criteria and selection of robots depend on the concrete construction process, which includes six common functional levels: distribution, leveling and compaction, floor finishing, surface painting, 3D printing, and surveillance. Misunderstood functions and the improper adjustment of construction robots may lead to increased cost, reduced effectiveness, and restricted application scenarios. Our review identifies current commercial and recently studied concrete construction robots to facilitate the standardization and optimization of robotic construction design. Moreover, this study may be able to guide future research and technology development efforts for autonomous robots in concrete construction.

In the existing research on prosthetic footplates, rehabilitation insoles, and robot feet, the cushioning parts are basically based on simple mechanisms and elastic pads. Most of them are unable to provide adequate impact resistance especially during contact with the ground. This paper developed a bioinspired heel pad by optimizing the inner structures inspired from human heel pad which has great cushioning performance. The distinct structures of the human heel pad were determined through magnetic resonance imaging (MRI) technology and related literatures. Five-layer pads with and without inner structures by using two materials (soft rubber and resin) were obtained, resulting in four bionic heel pads. Three finite element simulations (static, impact, and walking) were conducted to compare the cushioning effects in terms of deformations, ground reactions, and principal stress. The optimal pad with bionic structures and soft rubber material reduced 28.0% peak vertical ground reaction force (GRF) during walking compared with the unstructured resin pad. Human walking tests by a healthy subject wearing the 3D printed bionic pads also showed similar findings, with an almost 20% decrease in peak vertical GRF at normal speed. The soft rubber heel pad with bionic structures has the best cushioning performance, while the unstructured resin pad depicts the poorest. This study proves that with proper design of the inner structures and materials, the bionic pads will demonstrate distinct cushioning properties, which could be applied to the engineering fields, including lower limb prosthesis, robotics, and rehabilitations.

The need for the automated facial expression analysis arises in various clinical settings involving mental and physical health assessment of older adults. However, the effect of age (young versus old) and ability (healthy versus physical or cognitive impairment) on the performance of available methods has not yet been investigated. In this paper, we demonstrate a bias affecting the performance of common facial landmark detection and expression recognition algorithms on the faces of older adults with dementia. We also investigate the ways of mitigating this bias via the addition of representative training examples. Results show that landmark placement is less accurate when tested on the faces of individuals with dementia as compared to older adults who are cognitively healthy. Retraining or fine-tuning the methods with images of older adults' faces improves the performance significantly, but the gap between older adults with versus without dementia persists. As the interest in using facial analysis methods in clinical applications grows, results of this study: 1) highlight the limitations of the existing models when applied to clinical populations and 2) shed light on methods of addressing these limitations as well as the need to develop algorithms designed to be fair with respect to variables such as age and ability.

Three-dimensional fluorescence is currently studied by methods such as parallel factor analysis (PARAFAC), fluorescence regional integration (FRI), and principal component analysis (PCA). There are also many studies combining convolutional neural networks at present, but there is no one method recognized as the most effective among the methods combining convolutional neural networks and 3D fluorescence analysis. Based on this, we took some samples from the actual environment for measuring 3D fluorescence data and obtained a batch of public datasets from the internet species. Firstly, we preprocessed the data (including two steps of PARAFAC analysis and CNN dataset generation), and then we proposed a 3D fluorescence classification method and a components fitting method based on VGG16 and VGG11 convolutional neural networks. The VGG16 network is used for the classification of 3D fluorescence data with a training accuracy of 99.6% (as same as the PCA + SVM method (99.6%)). Among the component maps fitting networks, we comprehensively compared the improved LeNet network, the improved AlexNet network, and the improved VGG11 network, and finally selected the improved VGG11 network as the component maps fitting network. In the improved VGG11 network training, we used the MSE loss function and cosine similarity to judge the merit of the model, and the MSE loss of the network training reached 4.6 × 10−4 (characterizing the variability of the training results and the actual results), and we used the cosine similarity as the accuracy criterion, and the cosine similarity of the training results reached 0.99 (comparison of the training results and the actual results). The network performance is excellent. The experiments demonstrate that the convolutional neural network has a great application in 3D fluorescence analysis.

Accurate morphological information on aortic valve cusps is critical in treatment planning. Image segmentation is necessary to acquire this information, but manual segmentation is tedious and time consuming. In this paper, we propose a fully automatic aortic valve cusps segmentation method from CT images by combining two deep neural networks, spatial configuration-Net for detecting anatomical landmarks and U-Net for segmentation of aortic valve components. A total of 258 CT volumes of end systolic and end diastolic phases, which include cases with and without severe calcifications, were collected and manually annotated for each aortic valve component. The collected CT volumes were split 6:2:2 for the training, validation and test steps, and our method was evaluated by five-fold cross validation. The segmentation was successful for all CT volumes with 69.26 s as mean processing time. For the segmentation results of the aortic root, the right-coronary cusp, the left-coronary cusp and the non-coronary cusp, mean Dice Coefficient were 0.95, 0.70, 0.69, and 0.67, respectively. There were strong correlations between measurement values automatically calculated based on the annotations and those based on the segmentation results. The results suggest that our method can be used to automatically obtain measurement values for aortic valve morphology.

Chimonanthus praecox, a member of the Calycanthaceae family, is a unique, traditional, and famous flowering economic tree species in China. Despite the existence of several varieties, only a few cultivars have been formally named. Currently, expression sequence tag–simple sequence repeat (EST-SSR) markers are extensively used to identify different species and varieties; a large number of microsatellites can be identified from transcriptome databases. A total of 162,638 unigenes were assembled using RNA-seq; 82,778 unigenes were annotated using the Nr, Nt, Swiss-Prot, Pfam, GO, KOG, and KEGG databases. In total, 13,556 SSR loci were detected from 11,691 unigenes, with trinucleotide repeat motifs being the most abundant among the six repeat motifs. To develop the markers, 64,440 pairs of SSR primers with polymorphism potential were designed, and 75 pairs of primers were randomly selected for amplification. Among these markers, seven pairs produced amplified fragments of the expected size with high polymorphism. Using these markers, 12 C. praecox varieties were clustered into two monophyletic clades. Microsatellites in the transcriptome of C. praecox exhibit rich types, strong specificity, and great polymorphism potential. These EST-SSR markers serve as molecular technical methods for identifying different varieties of C. praecox and facilitate the exploration of a large number of candidate genes associated with important traits. [ABSTRACT FROM AUTHOR]