In this paper, a frequency selective surface (FSS) based shared-aperture antenna is designed for 5G/Wi-Fi applications. The 5G and Wi-Fi channel are separated to achieve different polarizations and simultaneous work. The proposed antenna is of ±45°-polarization in the 5G N79 band and vertical polarization in the Wi-Fi5.8 GHz band. The antenna is vertically stacked with the N79 band antenna located above the Wi-Fi band antenna. The N79 antenna is composed of FSS units with transmissive characteristics in the Wi-Fi band, and serves as an electromagnetically transparent surface to avoid blocking the Wi-Fi antenna. A prototype of our design is fabricated, assembled and tested, and measured results show that the prototype is able to cover the entire N79 band (4.1–5.2 GHz, 23.7%) and Wi-Fi5.8 GHz band (5.73–5.89 GHz, 2.8%). Measured average gain is 8.2 and 7.8 dBi in the N79 and Wi-Fi band, respectively, and radiation efficiency is over 86 and 80%. The proposed design exhibits separated channels, tri-polarizations, high gain and compact size, which is sufficient for regular 5G/Wi-Fi applications. The antenna also achieves a relatively wide and highly consistent signal coverage in the two bands, making it suitable for 5G/Wi-Fi multi-function communication and indoor 5G blind compensation.

Particle-resolved direct numerical simulations are employed to investigate the particle–particle drag force in the bidisperse gas–particle suspensions where the particles are smooth and the single-particle velocity distribution function is Maxwellian. The particle Reynolds number ranges from 6.7 to 123.8, and in this range the particle inertia is high enough that the lubrication force is not essential. It is found that the relation derived by the kinetic theory of granular flow (KTGF) highly overestimates the particle–particle drag force. This is because the pre-collision velocities of colliding particles are not completely uncorrelated with each other. From the time sequence of collision events, it is observed that the particle pair that has just collided will probably collide again after a short time due to the restriction of the particle motion in dense suspensions. Since the post-collision velocities of the first collision cannot relax entirely in such a short time, the relative velocity before the subsequent collision is statistically smaller than the domain-averaged relative velocity. Consequently, the particle–particle drag force is over-predicted when the domain-averaged relative velocity is used. For this reason, this work assumes that the particle–particle drag force is determined by the relative velocity within a local region near large particles. When the local region is set to be the spherical shells centred on the centres of large particles and with an outer radius of a mean free path of small particles, the KTGF-based relation can reasonably predict the particle–particle drag force.

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.

High Mountain Asia (HMA) glaciers are critical water reserves for montane regions, which are readily influenced by climate change. The glacier mass balance during 2000–2021 over HMA was estimated by comparing the elevations from ICESat-2 and the NASADEM. Radar penetration depth could be one of the intrinsic error sources in estimating glacier mass balance by using NASADEM. Therefore, we doubled elevation differences between the X-band Shuttle Radar Topography Missions (SRTMs) and NASADEM to estimate the potential error. The spatial characteristics of the altitude-dependent penetration depth can be detected in most sub-regions of HMA. Relatively deep penetrations in the Himalaya (2.3–3.7 m) and Hissar Alay (4.3 m) regions and small penetrations in the south-eastern HMA (1.0 m) were observed. The HMA region experienced a significant mass loss at a rate of −0.18 ± 0.12 m w.e. a−1, in which the Hengduan Shan exhibited the highest mass loss of −0.62 ± 0.10 m w.e. a−1, the West Kun Lun experienced a substantial mass gain of 0.23 ± 0.13 m w.e. a−1, and the Karakoram showed a more or less balance. Our results are in agreement with previous studies that assessed the mass balance of HMA glaciers from different methods.

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.

We report a generation of energetic protons by the interaction of a high-energy electron driving beam with an underdense plasma slab. After an interaction period of approximately 4000 fs, a proton beam with maximum energy greater than 250 MeV can be achieved by applying a driving beam with energy 1.0 GeV to a 200 $\mathrm {\mu }$m plasma slab. Our two-dimensional particle-in-cell simulations also show that the proton acceleration process can be divided into two stages. In the first stage, a strong positive longitudinal electric field appears near the rear boundary of the plasma slab after the driving beam has passed through it. This acceleration process is similar to the target normal sheath acceleration scheme by the interaction between intense pulsed laser with overdense plasma targets. In the second stage, the accelerated protons experience a long-range acceleration process with a two-stream instability between the high-energy driving beam and the proton beam. Further analyses show that this accelerated proton beam is equipped with the property of good collimation and high energy. This scheme presents a new way for proton or ion acceleration on some special occasions.

Fibronectin type III domain-containing protein 5 (FNDC5) is a transmembrane protein and the precursor of irisin, which serves as a systemic exerkine/myokine with multiple origins. Since its discovery in 2012, this hormone-like polypeptide has rapidly evolved to a component significantly involved in a gamut of metabolic dysregulations and various liver diseases. After a decade of extensive investigation on FNDC5/irisin, we are still surrounded by lots of open questions regarding its diagnostic and therapeutic values. In this review, we first concentrated on the structure–function relationship of FNDC5/irisin. Next, we comprehensively summarised the current knowledge and research findings regarding pathogenic roles/therapeutic applications of FNDC5/irisin in the context of non-alcoholic fatty liver disease, fibrosis, liver injury due to multiple detrimental insults, hepatic malignancy and intrahepatic cholestasis of pregnancy. Moreover, the prominent molecules involved in the underlying mechanisms and signalling pathways were highlighted. As a result, emerging evidence reveals FNDC5/irisin may act as a proxy for diagnosing liver disease pathology, a sensitive biomarker for assessing damage severity, a predisposing factor for surveilling illness progression and a treatment option with protective/preventive impact, all of which are highly dependent on disease grading and contextually pathological features.

A high-frequency short-pulsed stroboscopic micro-visual system was employed to capture the transient image sequences of a periodically in-plane working micro-electro-mechanical system (MEMS) devices. To demodulate the motion parameters of the devices from the images, we developed the feature point matching (FPM) algorithm based on Speeded-Up Robust Features (SURF). A MEMS gyroscope, vibrating at a frequency of 8.189 kHz, was used as a testing sample to evaluate the performance of the proposed algorithm. Within the same processing time, the SURF-based FPM method demodulated the velocity of the in-plane motion with a precision of 10−5 pixels of the image, which was two orders of magnitude higher than the template-matching and frame-difference algorithms.

This paper proposes a novel speed optimisation scheme for unmanned sailboats by sliding mode extremum seeking control (SMESC) without steady-state oscillation. In the sailing speed optimisation scheme, an initial sail angle of attack is first computed by a piecewise constant function in the feed forward block, which ensures a small deviation between sailing speed and the maximum speed. Second, the sailing speed approaches to maximum gradually by extremum search control (ESC) in the feedback block. In SMESC without steady-state oscillation, a switching law is designed to carry out the control transformation, so that the speed optimisation system carries out SMESC in the first convergence phase and ESC without steady-state oscillation in the second stability phase. This scheme combines the advantages of both control algorithms to maintain a faster convergence rate and to eliminate steady-state oscillation. Furthermore, the strict stability of the speed optimisation system is proved in this paper. Finally, we test a 12-m mathematical model of an unmanned sailboat in the simulation to demonstrate the effectiveness and robustness of this speed optimisation scheme.

The aim of the present study was to compare the rate of preterm birth (PTB) and growth from birth to 18 years between twins conceived by in vitro fertilization (IVF) and twins conceived by spontaneous conception (SC) in mainland China. The retrospective cohort study included 1164 twins resulting from IVF and 25,654 twins conceived spontaneously, of which 494 from IVF and 6338 from SC were opposite-sex twins. PTB and low birth weight (LBW), and growth, including length/height and weight, were compared between the two groups at five stages: infancy (0 year), toddler period (1–2 years), preschool (3–5 years), primary or elementary school (6–11 years), and adolescence (10–18 years). Few statistically significant differences were found for LBW and growth between the two groups after adjusting for PTB and other confounders. Twins born by IVF faced an increased risk of PTB compared with those born by SC (adjusted odds ratio [aOR] 8.21, 95% confidence interval [CI] [3.19, 21.13], p < .001 in all twins and aOR 10.12, 95% CI [2.32, 44.04], p = .002 in opposite-sex twins). Twins born by IVF experienced a similar growth at five stages (0–18 years old) when compared with those born by SC. PTB risk, however, is significantly higher for twins conceived by IVF than those conceived by SC.

Prolonged sitting in a fixed or constrained position exposes aircraft passengers to long-term static loading of their bodies, which has deleterious effects on passengers’ comfort throughout the duration of the flight. The previous studies focused primarily on office and driving sitting postures and few studies, however, focused on the sitting postures of passengers in aircraft. Consequently, the aim of the present study is to detect and recognize the sitting postures of aircraft passengers in relation to sitting discomfort. A total of 24 subjects were recruited for the experiment, which lasted for 2 h. Furthermore, a total of 489 sitting postures were extracted and the pressure data between subjects and seat was collected from the experiment. After the detection of sitting postures, eight types of sitting postures were classified based on key parts (trunk, back, and legs) of the human bodies. Thereafter, the eight types of sitting postures were recognized with the aid of pressure data of seat pan and backrest employing several machine learning methods. The best classification rate of 89.26% was obtained from the support vector machine (SVM) with radial basis function (RBF) kernel. The detection and recognition of the eight types of sitting postures of aircraft passengers in this study provided an insight into aircraft passengers’ discomfort and seat design.

In clinical environments, orthopedic implants are associated with a risk of infection during implantation. However, the growth paths of bacteria on metal, which is nontransparent, are difficult to observe. In this study, we visualized the DH5-alpha Escherichia coli bacterial growth path on the surface of magnesium by using scanning electron microscope (SEM) images and constructed a convolutional neural network-based artificial intelligence (AI) system to identify metal surfaces, bacteria, and its generated products to grade the growth stage of the bacteria implanted on the magnesium. The detection result of the E. coli growth stage by the AI system was close to that manually marked by experts, and it may greatly accelerate the investigation of the bacterial growth process in various types of metallic material.