In order to solve the problems of low power transfer efficiency (PTE) and limited distance in magnetic coupling resonant wireless power transfer (MCR-WPT) technology. In this paper, based on the magnetic field control ability of metamaterials (MMs), the way to improve the performance of MCR-WPT systems is studied. First, the influence of MMs on the coupling of MCR-WPT system is theoretically analyzed by establishing an equivalent circuit model. Through a series of simulations and experiments, the relationships between the PTE and the array and placement of the MM slab are investigated. The results demonstrate that when one MM slab is placed in the middle, near the Tx coil or the Rx coil, the optimal PTE can be obtained by inserting one slab with $6 \times 6$, $1 \times 1$, and $6 \times 6$ arrays, respectively. Moreover, the systems with multilayer MM slabs are also studied. The measured PTEs on one, two, and three layers of MM slabs can increase by 20.6%, 29.3%, and 22.6%, respectively. The efficiency improvement capability of one MM slab is better than three slabs but worse than two slabs. This paper discusses the application of MMs in MCR-WPT systems, which has a certain reference significance.

This work proposes a machine-learning or artificial intelligence (AI) control of a low-drag Ahmed body with a rear slant angle φ = 35° with a view to finding strategies for efficient drag reduction (DR). The Reynolds number Re investigated is 1.7 × 105 based on the square root of the body cross-sectional area. The control system comprises of five independently operated arrays of steady microjets blowing along the edges of the rear window and vertical base, twenty-six pressure taps on the rear end of the body and a controller based on an ant colony algorithm for unsupervised learning of a near-optimal control law. The cost function is designed such that both DR and control power input are considered. The learning process of the AI control discovers forcing that produces a DR up to 18 %, corresponding to a drag coefficient reduction of 0.06, greatly exceeding any previously reported DR for this body. Furthermore, the discovered forcings may provide alternative solutions, i.e. a tremendously increased control efficiency given a small sacrifice in DR. Extensive flow measurements performed with and without control indicate significant alterations in the flow structure around the body, such as flow separation over the rear window, recirculation bubbles and C-pillar vortices, which are linked to the pressure rise on the window and base. The physical mechanism for DR is unveiled, along with a conceptual model for the altered flow structure under the optimum control or biggest DR. This mechanism is further compared with that under the highest control efficiency.

Zonal jets and inertia–gravity waves are ubiquitous on planets such as Earth, Jupiter and Saturn. Motivated by the modification of energy flux of balanced flow by inertia–gravity waves, this paper studies the impact of near-inertial waves (NIWs) on zonal jets on a $\beta$-plane. Using a two-dimensional quasi-geostrophic and NIW coupled system on a $\beta$-plane (Xie & Vanneste, J. Fluid Mech., vol. 774, 2015, pp. 143–169), we find NIWs catalytically impact several features of zonal jets. The NIWs inhibit jet formation due to the waves’ catalytic induction of downscale mean energy flux. As the strength of NIWs increases, a critical point exists beyond which zonal jets are annihilated. The jet spacing is captured by the Rhines scale $L\sim \sqrt {U/\beta }$ with $U$ estimated from the upscale energy flux induced by the mean flow alone, which again shows that the NIWs’ impact is catalytic. Also, the temporal asymmetry of NIWs leads to the spatial asymmetry of jet dynamics. The jet profiles are asymmetric with a stronger shear on the left flank. And similar to the left turning of vortex dipole under the impact of NIWs, the NIW-modified jets migrate poleward. The NIWs also show a catalytic role in jet migration: the net momentum flux directly induced by NIWs is of secondary importance in the zonal mean momentum dynamics and impedes jet migration, while the advective effect of NIW-modified mean flow dominates the jet migration velocity.

Suppose that X and Y are two real normed spaces. A map $f:X\rightarrow Y$ is called a min-phase-isometry if it satisfies

$$ \begin{align*} \min\{\|f(x)+f(y)\|,\|f(x)-f(y)\|\}=\min\{\|x+y\|,\|x-y\|\} \quad (x,y\in X). \end{align*} $$

We present properties of min-phase-isometries in the case that Y is strictly convex and show that if a min-phase-isometry f (not necessarily surjective) fixes the origin, then it is phase-equivalent to a linear isometry, that is, $f(x)=\varepsilon (x)g(x)$ for $x\in X$ , where $g:X\rightarrow Y$ is a linear isometry and $\varepsilon $ is a map from X to $\{-1,1\}$ .

Hormone-sensitive lipase (HSL) is one of the rate-determining enzymes in the hydrolysis of TAG, playing a crucial role in lipid metabolism. However, the role of HSL-mediated lipolysis in systemic nutrient homoeostasis has not been intensively understood. Therefore, we used CRISPR/Cas9 technique and Hsl inhibitor (HSL-IN-1) to establish hsla-deficient (hsla -/-) and Hsl-inhibited zebrafish models, respectively. As a result, the hsla -/- zebrafish showed retarded growth and reduced oxygen consumption rate, accompanied with higher mRNA expression of the genes related to inflammation and apoptosis in liver and muscle. Furthermore, hsla -/- and HSL-IN-1-treated zebrafish both exhibited severe fat deposition, whereas their expressions of the genes related to lipolysis and fatty acid oxidation were markedly reduced. The TLC results also showed that the dysfunction of Hsl changed the whole-body lipid profile, including increasing the content of TG and decreasing the proportion of phospholipids. In addition, the systemic metabolic pattern was remodelled in hsla -/- and HSL-IN-1-treated zebrafish. The dysfunction of Hsl lowered the glycogen content in liver and muscle and enhanced the utilisation of glucose plus the expressions of glucose transporter and glycolysis genes. Besides, the whole-body protein content had significantly decreased in the hsla -/- and HSL-IN-1-treated zebrafish, accompanied with the lower activation of the mTOR pathway and enhanced protein and amino acid catabolism. Taken together, Hsl plays an essential role in energy homoeostasis, and its dysfunction would cause the disturbance of lipid catabolism but enhanced breakdown of glycogen and protein for energy compensation.

In recent years, autonomous control based on contact inspections in unknown environments is a new hot and difficult point in robotics research. This paper presents a new control law for unmanned aerial manipulator (UAM) to perform contact inspection tasks on vertical surfaces. The selected circular image feature decouples the position and attitude of UAM, so an image-based impedance control is proposed to control the position and track the contact force. The developed controller uses geometric methods to control the attitude. In addition, the designed aerial manipulator decouples the roll and pitch of the UAV from the UAV, which improves the system’s stability. Experiments have been carried out to demonstrate the feasibility of this method.

Several studies have been conducted on collision avoidance (CA) and path planning for maritime autonomous surface ships (MASS) based on artificial potential field (APF) and electronic navigation chart (ENC) data. However, to date, accurate, highly efficient, and automatic modelling of complicated geometry environment potential fields (EPFs) has not been realised. In this study, an accurate EPF model is established using ENC data to describe different types of obstacles, navigable areas, and non-navigable areas. The implicit equations of complex polygons are constructed based on the R-function theory, and the discrete-convex hull method is introduced to realise the automatic modelling of EPF. Moreover, collaborative CA and obstacle avoidance (OA) experiments are designed and conducted in a simulated environment and based on the ENC data. The results show that the proposed EPF modelling method is accurate, reliable, and time-efficient even with numerous ENC data and complex shapes owing to the R-function representation for geometric objects and discrete-convex hull method. The combination of improved APF and EPF models is proven to be effective for CA and OA. This paper presents a practical EPF modelling approach for APF-based ship path planning.

We report the experimental results of the commissioning phase in the 10 PW laser beamline of the Shanghai Superintense Ultrafast Laser Facility (SULF). The peak power reaches 2.4 PW on target without the last amplifying during the experiment. The laser energy of 72 ± 9 J is directed to a focal spot of approximately 6 μm diameter (full width at half maximum) in 30 fs pulse duration, yielding a focused peak intensity around 2.0 × 1021 W/cm2. The first laser-proton acceleration experiment is performed using plain copper and plastic targets. High-energy proton beams with maximum cut-off energy up to 62.5 MeV are achieved using copper foils at the optimum target thickness of 4 μm via target normal sheath acceleration. For plastic targets of tens of nanometers thick, the proton cut-off energy is approximately 20 MeV, showing ring-like or filamented density distributions. These experimental results reflect the capabilities of the SULF-10 PW beamline, for example, both ultrahigh intensity and relatively good beam contrast. Further optimization for these key parameters is underway, where peak laser intensities of 1022–1023 W/cm2 are anticipated to support various experiments on extreme field physics.

Aiming at the problem of low accuracy of robot joint fault diagnosis, a fault diagnosis method of robot joint based on BP neural network is designed. In this paper, the UR10 robot is taken as the research object, and the end pose data of the robot are collected in real time. By injecting different joint errors and changing the sampling frequency, the joint fault database is collected and established, and the BP neural network is used for training to obtain the robot neural network fault diagnosis model. The fault diagnosis model can output the joint fault of the input end pose data. And we analyzed the influence of different joint angle errors and different training sets on the accuracy of joint fault diagnosis of the robot. The results show that when the sampling frequency is 250 Hz, the simulation result of joint fault diagnosis accuracy with the fault degree of 0.5° is 99.17%, and the experimental result is 97.87%. Compared with traditional data-driven methods, it has higher accuracy and diagnostic efficiency, and compared with existing machine learning methods, it also achieves a high accuracy while reducing the network complexity. The effectiveness of the BP neural network robot joint fault diagnosis method is verified by experiments.