For a hypersonic-speed aircraft with a flat fuselage structure that has narrow space for a traditional wheel-type landing gear retraction, a novel type of wheel-ski landing gear is designed, which is different from traditional landing gears in force distribution and actuation methods. In order to capture the direction control performance of an aircraft with the wheel-ski landing gear, the aircraft ground taxiing nonlinear dynamic mathematical model is built based on a certain type of aircraft data. The experiment of the wheel-ski landing gear actuator and the differential brake control system is carried out to verify that the electric wheel-ski actuator model with the pressure sensor is in good agreement with the test results, indicating the model validity and the speediness of the differential brake response. Then a new fuzzy combined direction rectifying control law is designed based on the optimisation method and the fuzzy control theory. Comparing with the PD wheel-ski differential brake control, the direction rectifying efficiencies increase higher than 140% during the whole taxiing process. In addition, the combined control law can also decrease the overshoots of the yaw angle responses effectively. Finally, the stability and robustness of the designed combined direction control law are verified under various working conditions.

The dimension of models derived on the basis of data is commonly restricted by the number of observations, or in the context of monitored systems, sensing nodes. This is particularly true for structural systems, which are typically high-dimensional in nature. In the scope of physics-informed machine learning, this article proposes a framework—termed neural modal ordinary differential equations (Neural Modal ODEs)—to integrate physics-based modeling with deep learning for modeling the dynamics of monitored and high-dimensional engineered systems. In this initiating exploration, we restrict ourselves to linear or mildly nonlinear systems. We propose an architecture that couples a dynamic version of variational autoencoders with physics-informed neural ODEs (Pi-Neural ODEs). An encoder, as a part of the autoencoder, learns the mappings from the first few items of observational data to the initial values of the latent variables, which drive the learning of embedded dynamics via Pi-Neural ODEs, imposing a modal model structure on that latent space. The decoder of the proposed model adopts the eigenmodes derived from an eigenanalysis applied to the linearized portion of a physics-based model: a process implicitly carrying the spatial relationship between degrees-of-freedom (DOFs). The framework is validated on a numerical example, and an experimental dataset of a scaled cable-stayed bridge, where the learned hybrid model is shown to out perform a purely physics-based approach to modeling. We further show the functionality of the proposed scheme within the context of virtual sensing, that is, the recovery of generalized response quantities in unmeasured DOFs from spatially sparse data.

Adolescence is a significant period for the formation of relationship networks and the development of internalizing problems. With a sample of Chinese adolescents (N = 3,834, 52.01% girls, Mage = 16.68 at Wave 1), the present study aimed to identify the configuration of adolescents’ relationship qualities from four important domains (i.e., relationship quality with mother, father, peers, and teachers) and how distinct profiles were associated with the development of internalizing problems (indicated by depressive and anxiety symptoms) across high school years. Latent profile analysis identified a five-profile configuration with four convergent profiles (i.e., relationship qualities with others were generally good or bad) and one “Father estrangement” profile (i.e., the relationship quality with others were relatively good but that with father was particularly poor). Further conditional latent growth curve analysis indicated the “Father estrangement” profile was especially vulnerable to an increase in the internalizing problems as compared with other relationship profiles. This study contributes to understanding the characteristics of interpersonal relationship qualities and their influences on adolescent internalizing problems in a non-Western context. Results were further discussed from a culturally specific perspective.

The aim of this paper is twofold: the first aim is to formulate and validate a multi-scale discrete Boltzmann method (DBM) based on density functional kinetic theory for thermal multiphase flow systems, ranging from continuum to transition flow regime; the second aim is to present some new insights into the thermo-hydrodynamic non-equilibrium (THNE) effects in the phase separation process. Methodologically, for bulk flow, DBM includes three main pillars: (i) the determination of the fewest kinetic moment relations, which are required by the description of significant THNE effects beyond the realm of continuum fluid mechanics; (ii) the construction of an appropriate discrete equilibrium distribution function recovering all the desired kinetic moments; (iii) the detection, description, presentation and analysis of THNE based on the moments of the non-equilibrium distribution ( $f-f^{(eq)}$). The incorporation of appropriate additional higher-order thermodynamic kinetic moments considerably extends the DBM's capability of handling larger values of the liquid–vapour density ratio, curbing spurious currents, and ensuring mass/momentum/energy conservation. Compared with the DBM with only first-order THNE (Gan et al., Soft Matt., vol. 11 (26), 2015, pp. 5336–5345), the model retrieves kinetic moments beyond the third-order super-Burnett level, and is accurate for weak, moderate and strong THNE cases even when the local Knudsen number exceeds $1/3$. Physically, the ending point of the linear relation between THNE and the concerned physical parameter provides a distinct criterion to identify whether the system is near or far from equilibrium. Besides, the surface tension suppresses the local THNE around the interface, but expands the THNE range and strengthens the THNE intensity away from the interface through interface smoothing and widening.

Coronavirus disease 2019 (COVID-19) asymptomatic cases are hard to identify, impeding transmissibility estimation. The value of COVID-19 transmissibility is worth further elucidation for key assumptions in further modelling studies. Through a population-based surveillance network, we collected data on 1342 confirmed cases with a 90-days follow-up for all asymptomatic cases. An age-stratified compartmental model containing contact information was built to estimate the transmissibility of symptomatic and asymptomatic COVID-19 cases. The difference in transmissibility of a symptomatic and asymptomatic case depended on age and was most distinct for the middle-age groups. The asymptomatic cases had a 66.7% lower transmissibility rate than symptomatic cases, and 74.1% (95% CI 65.9–80.7) of all asymptomatic cases were missed in detection. The average proportion of asymptomatic cases was 28.2% (95% CI 23.0–34.6). Simulation demonstrated that the burden of asymptomatic transmission increased as the epidemic continued and could potentially dominate total transmission. The transmissibility of asymptomatic COVID-19 cases is high and asymptomatic COVID-19 cases play a significant role in outbreaks.

In this study, a toroidal quartz ( $20\overline{2}3$ ) crystal is designed for monochromatic X-ray imaging at 72.3°. The designed crystal produces excellent images of a laser-produced plasma emitting He-like Ti X-rays at 4.75 keV. Based on the simulations, the imaging resolutions of the spherical and toroidal crystals in the sagittal direction are found to be 15 and 5 μm, respectively. Moreover, the simulation results show that a higher resolution image of the source can be obtained by using a toroidal crystal. An X-ray backlight imaging experiment is conducted using 4.75 keV He-like Ti X-rays, a 3 × 3 metal grid, an imaging plate and a toroidal quartz crystal with a lattice constant of 2d = 0.2749 nm. The meridional and sagittal radii of the toroidal α-quartz crystal are 295.6 and 268.5 mm, respectively. A highly resolved image of the microgrid, with a spatial resolution of 10 μm, is obtained in the experiment. By using similar toroidal crystal designs, the application of a spatially resolved spectrometer with high-resolution X-ray imaging ability is capable of providing imaging data with the same magnification ratio in the sagittal and meridional planes.

The selection of high-quality sperms is critical to intracytoplasmic sperm injection, which accounts for 70–80% of in vitro fertilization (IVF) treatments. So far, sperm screening is usually performed manually by clinicians. However, the performance of manual screening is limited in its objectivity, consistency, and efficiency. To overcome these limitations, we have developed a fast and noninvasive three-stage method to characterize morphology of freely swimming human sperms in bright-field microscopy images using deep learning models. Specifically, we use an object detection model to identify sperm heads, a classification model to select in-focus images, and a segmentation model to extract geometry of sperm heads and vacuoles. The models achieve an F1-score of 0.951 in sperm head detection, a z-position estimation error within ±1.5 μm in in-focus image selection, and a Dice score of 0.948 in sperm head segmentation, respectively. Customized lightweight architectures are used for the models to achieve real-time analysis of 200 frames per second. Comprehensive morphological parameters are calculated from sperm head geometry extracted by image segmentation. Overall, our method provides a reliable and efficient tool to assist clinicians in selecting high-quality sperms for successful IVF. It also demonstrates the effectiveness of deep learning in real-time analysis of live bright-field microscopy images.

It is generally accepted that high-oleic crops have at least 70% oleate. As compared to their normal-oleic counterparts, oil and food products made from high-oleic peanut have better keeping quality and are much healthier. Therefore, high-oleic peanut is well recognized by processors and consumers. However, owing to the limited availability of high-oleic donors, most present-day high-oleic peanut varietal releases merely have F435 type FAD2 mutations. Through screening of a mutagenized peanut population of 15L46, a high-yielding peanut line with desirable elliptical oblong large seeds, using near infrared model for predicting oleate content in individual single seeds, high-oleic peanut mutants were identified. Sequencing FAD2A and FAD2B of the mutants along with the wild type revealed that these mutants possessed G448A FAD2A (F435 type FAD2A mutation) and G558A FAD2B (non-F435 type FAD2B mutation). Expression of the wild and mutated type FAD2B in yeast verified that the functional mutation contributed to the high-oleic phenotype in these mutants. The mutants provided additional high-oleic donors to peanut quality improvement.

Nosema bombycis is a destructive and specific intracellular parasite of silkworm, which is extremely harmful to the silkworm industry. N. bombycis is considered as a quarantine pathogen of sericulture because of its long incubation period and horizontal and vertical transmission. Herein, two single-chain antibodies targeting N. bombycis hexokinase (NbHK) were cloned and expressed in fusion with the N-terminal of Slmb (a Drosophila melanogaster FBP), which contains the F-box domain. Western blotting demonstrated that Sf9-III cells expressed NSlmb–scFv-7A and NSlmb–scFv-6H, which recognized native NbHK. Subsequently, the NbHK was degraded by host ubiquitination system. When challenged with N. bombycis, the transfected Sf9-III cells exhibited better resistance relative to the controls, demonstrating that NbHK is a prospective target for parasite controls and this approach represents a potential solution for constructing N. bombycis-resistant Bombyx mori.

For the path planning of autonomous underwater vehicles (AUVs) in the ocean environment, in addition to the planned path length and safe obstacle avoidance, it is also necessary to pay attention to the impact of ocean currents on the planned path. Therefore, this paper improves the original D* algorithm, and adds the obstacle cost item and the steering angle cost item as constraints on the basis of the original cost function, thus ensuring the navigation safety of the AUV. Considering that ocean currents have a greater impact on the energy consumption of AUVs, this paper establishes a cost model for the impact of ocean currents on AUV energy consumption and applies it to the D* path planning algorithm, so that AUVs can use ocean currents to reduce energy consumption, which can be seen through simulation experiments. The simulation results show that the improvement of the algorithm can plan an optimal energy consumption path.

The paper deals with the workspace-based optimization of a novel humanoid robotic arm. The eight-degree-of-freedom hybrid manipulator that conforms to the kinematics characteristics of the human arm is briefly introduced. According to the structural features of this mechanism and the requirements of tasks in the complex environment, the workspace is divided into three parts, the orientation space of the humanoid shoulder joint, the position space of the humanoid elbow joint, and the active orientation space of the end-moving platform. Moreover, a multi-parameter planar model is proposed for the optimization problem with multidimensional parameters and highly nonlinear constraints. Based on the visualized optimization result, the coupling effect of each parameter on the corresponding workspace is clearly presented. Considering the compactness and the processing and assembling technology of this mechanism, a set of structural parameters satisfying the workspace-based optimization objective is obtained. Simulation results show that the corresponding workspace of the three parts has increased significantly by the factor of 1.45, 1.68, and 1.3, respectively.

Reconstructing the history of elite communication in ancient China benefits from additional archaeological evidence. We combine textual analysis with new human stable carbon and nitrogen isotope data from two Chu burials in the Jingzhou area to reveal significant dietary differences among Chu nobles of the middle Warring States period (c. 350 BC). This research provides important new information on the close interaction between the aristocratic families of the Qin and Chu.

Viruses completely rely on the energy and metabolic systems of host cells for life activities. Viral infections usually lead to cytopathic effects and host diseases. To date, there are still no specific clinical vaccines or drugs against most viral infections. Therefore, understanding the molecular and cellular mechanisms of viral infections is of great significance to prevent and treat viral diseases. A variety of viral infections are related to the p38 MAPK signalling pathway, and p38 is an important host factor in virus-infected cells. Here, we introduce the different signalling pathways of p38 activation and then summarise how different viruses induce p38 phosphorylation. Finally, we provide a general summary of the effect of p38 activation on virus replication. Our review provides integrated data on p38 activation and viral infections and describes the potential application of targeting p38 as an antiviral strategy.

The current study used behavioural and electroencephalograph measures to compare the transferability of three home-based interventions — cognitive training (CT), neurofeedback training (NFT), and CT combined with NFT — for reducing symptoms in children with attention-deficit/hyperactivity disorder (AD/HD). Following a multiple-baseline single-case experimental design, twelve children were randomised to a training condition. Each child completed a baseline phase, followed by an intervention phase. The intervention phase consisted of 20 sessions of at-home training. Tau-U analysis and standardised visual analysis were adopted to detect effects. Results showed that CT improved inhibitory function and NFT improved alpha EEG activity and working memory. The combined condition, which was a reduced ‘dose’ of CT and NFT, did not show any improvements. The three conditions did not alleviate AD/HD symptoms. While CT and NFT may have transfer effects on executive functions, considering the lack of improvement in symptoms, this study does not support CT and NFT on their own as a treatment for children with AD/HD.