In this paper, a highly integrated wideband 3 × 3 Nolen Matrix with inherent filtering characteristics is proposed. It is based on an arbitrary-phase-difference (A-PD) filtering coupler and phase compensation networks. The proposed A-PD filtering coupler, composed of three groups of coupled lines, offers outstanding advantages, including wide bandwidth, flat output distributions, high frequency selectivity, and compact structure. To address the challenges introduced by the series topology of the Nolen matrix, a differential phase shift network and a phase slope adjustment network are incorporated, ensuring a constant phase difference between stages and minimizing in-band phase errors at the output ports. By integrating the A-PD filtering coupler with the phase compensation networks, a compact Nolen matrix centered at 3.5 GHz is realized, occupying only 0.5 λg × 0.5 λg. Measurement results validate its excellent performance, demonstrating an overlapping bandwidth exceeding 50% under the criteria of 10-dB return loss, 3-dB passband, ±1 dB amplitude imbalance, and ±5° phase difference error. Furthermore, the design achieves over 15 dB stopband rejection.

In the realm of data-to-text generation tasks, the use of large language models (LLMs) has become common practice, yielding fluent and coherent outputs. Existing literature highlights that the quality of in-context examples significantly influences the empirical performance of these models, making the efficient selection of high-quality examples crucial. We hypothesize that the quality of these examples is primarily determined by two properties: their similarity to the input data and their diversity from one another. Based on this insight, we introduce a novel approach, Double Clustering-based In-Context Example Selection, specifically designed for data-to-text generation tasks. Our method involves two distinct clustering stages. The first stage aims to maximize the similarity between the in-context examples and the input data. The second stage ensures diversity among the selected in-context examples. Additionally, we have developed a batched generation method to enhance the token usage efficiency of LLMs. Experimental results demonstrate that, compared to traditional methods of selecting in-context learning samples, our approach significantly improves both time efficiency and token utilization while maintaining accuracy.

When a drop impinges onto a deep liquid pool, it can yield various splashing behaviours, leading to a crown-like structure along the free surface. Under high-speed impact conditions, the upper portion of the thin-walled crown may undergo necking and encapsulate a large bubble, which remains fascinating and is rarely discussed in the literature. In this work, we numerically study this physical process based on the volume-of-fluid and adaptive mesh refinement framework. Our meticulous observations have allowed us to unveil a spectrum of repeatable early-time jet behaviours, vorticity structures and crater evolution, underscoring the rich and complex nature of drop-impact phenomenon. We show that the interplay between aerodynamic pressure and surface tension on the liquid crown could play a significant role in its bending and surface closure. A regime map, incorporating both early-stage jet dynamics and overall bubble-canopy formation, is established across a wide parameter space. This study provides a comprehensive understanding of the diverse splashing regimes, offering insights into the fundamental characteristics of drop-impact phenomenon.

A Chebyshev-distributed 1 × 8 beamforming network with improved phase flatness is presented, where four beams with constant beam pointing and low sidelobe levels (SLL) can be generated. It consists of two arbitrary-amplitude 4 × 4 Blass-like matrices and one 1 × 8 switch control circuit. The newly introduced 4 × 4 Blass-like matrices can obtain arbitrary amplitude and phase differences by adjusting the transmission coefficient and phase of each unit. Besides, four output phase differences can be generated by controlling the 1 × 8 switch control circuit. An example is implemented for validation and phase compensation method is adopted for minimizing the phase difference error within the operated bandwidth to maintain constant beam pointing. Measurements show that the prototype exhibits output amplitude ratios of 0.143:0.341: 0.71:1:1:0.71:0.341:0.143, which fits the Chebyshev distribution. Under the criterion of |S11| < −10 dB, an overlapped fractional bandwidth of 24.1% is obtained. In addition, from 5.5 to 6.1 GHz (10.3%), the maximum amplitude and phase difference errors are 1.5 dB and 15°, respectively. Finally, the proposed network is connected to a 1 × 8 array. Within 10.3% bandwidth, the SLLs of less than −20 dB are realized without beam-pointing deviation.

The inverse dynamics model of an industrial robot can predict and control the robot’s motion and torque output, improving its motion accuracy, efficiency, and adaptability. However, the existing inverse rigid body dynamics models still have some unmodelled residuals, and their calculation results differ significantly from the actual industrial robot conditions. The bootstrap aggregating (bagging) algorithm is combined with a long short-term memory network, the linear layer is introduced as the network optimization layer, and a compensation method of hybrid inverse dynamics model for robots based on the BLL residual prediction algorithm is proposed to meet the above needs. The BLL residual prediction algorithm framework is presented. Based on the rigid body inverse dynamics of the Newton–Euler method, the BLL residual prediction network is used to perform error compensation on the inverse dynamics model of the Franka robot. The experimental results show that the hybrid inverse dynamics model based on the BLL residual prediction algorithm can reduce the average residuals of the robot joint torque from 0.5651 N·m to 0.1096 N·m, which improves the accuracy of the inverse dynamics model compared with those of the rigid body inverse dynamics model. This study lays the foundation for performing more accurate operation tasks using industrial robots.

This paper proposes a kinematic calibration method of a novel 5-degree-of-freedom double-driven parallel mechanism with the sub-closed loop on limbs. At first, considering the introduction of a sub-closed loop significantly increased the complexity and difficulty of kinematic error modeling, an equivalent transformation method is proposed for the limb with a sub-closed loop. Then kinematic error model of the parallel mechanism is established based on the closed-loop vector method and parasitic motion analysis, which is verified by virtual prototype technology. Because the full kinematic error model is generally redundant, error parameter identifiability analysis is carried out by QR decomposition of the identification Jacobian matrix, and the redundant parameters are removed. Additionally, the Sequence Forward Floating Search algorithm is utilized to optimize measurement configurations to reduce the influence of measurement noise. Finally, with a laser tracker as the measuring device, numerical simulations and experiments are implemented to verify the proposed kinematic calibration method. The experiment results show that average position and orientation errors are reduced from 2.778 mm and 1.115° to 0.263 mm and 0.176°, respectively, within the prescribed workspace.

A dual-band angular-stable transmissive linear to circular polarization converter based on metasurface is proposed and demonstrated in this work. The converter consists of three layers. The top and bottom layers are formed by an array of double split-ring layers. The unit cell of the central layer contains a square loop nesting a slant dipole. The split-rings create two resonances, enabling dual-band operation. The slant dipole and square loop are useful for improving the quality of circular polarization conversion. It is shown that the proposed polarization converter converts the incident linearly polarized wave into circularly polarized wave with opposite polarization modes over the frequency ranges of 8.77–10.58 and 17.59–19.88 GHz. The angular stability is up to 60° for 3 dB axial ratio. Moreover, the thickness of unit cell has a wavelength below 0.06 at the lower band. Compared with other designs in the literature, the structure bears merits of wideband response, high angular stability, and low-profile property within dual-band operational region. To validate the design, a sample prototype was designed, fabricated, and measured. The measured results are in good agreement with the simulated ones.

The present study focuses on two-dimensional direct numerical simulations of shallow-water breaking waves, specifically those generated by a wave plate at constant water depths. The primary objective is to quantitatively analyse the dynamics, kinematics and energy dissipation associated with wave breaking. The numerical results exhibit good agreement with experimental data in terms of free-surface profiles during wave breaking. A parametric study was conducted to examine the influence of various wave properties and initial conditions on breaking characteristics. According to research on the Bond number ($Bo$, the ratio of gravitational to surface tension forces), an increased surface tension leads to the formation of more prominent parasitic capillaries at the forwards face of the wave profile and a thicker plunging jet, which causes a delayed breaking time and is tightly correlated with the main cavity size. A close relationship between wave statistics and the initial conditions of the wave plate is discovered, allowing for the classification of breaker types based on the ratio of wave height to water depth, $H/d$. Moreover, an analysis based on inertial scaling arguments reveals that the energy dissipation rate due to breaking can be linked to the local geometry of the breaking crest $H_b/d$, and exhibits a threshold behaviour, where the energy dissipation approaches zero at a critical value of $H_b/d$. An empirical scaling of the breaking parameter is proposed as $b = a(H_b/d - \chi _0)^n$, where $\chi _0 = 0.65$ represents the breaking threshold and $n = 1.5$ is a power law determined through the best fit to the numerical results.

The present work is devoted to the analysis of drop impact on a deep liquid pool, focusing on the high-energy splashing regimes caused by large raindrops at high velocities. Such cases are characterized by short time scales and complex mechanisms, thus they have received very little attention until now. The BASILISK open-source solver is used to perform three-dimensional direct numerical simulations. The capabilities of octree adaptive mesh refinement techniques enable capturing of the small-scale features of the flow, while the volume of fluid approach combined with a balanced-force surface-tension calculation is applied to advect the volume fraction of the liquids and reconstruct the interfaces. The numerical results compare well with experimental visualizations: both the evolution of crown and cavity, the emanation of ligaments, the formation of bubble canopy and the growth of a downward-moving spiral jet that pierces through the cavity bottom, are correctly reproduced. Reliable quantitative agreements are also obtained regarding the time evolution of rim positions, cavity dimensions and droplet distributions through an observation window. Furthermore, simulation gives access to various aspects of the internal flows, which allows us to better explain the observed physical phenomena. Details of the early-time dynamics of bubble ring entrapment and splashing performance, the formation/collapse of bubble canopy and the spreading of drop liquid are discussed. The statistics of droplet size show the bimodal distribution in time, corroborating distinct primary mechanisms of droplet production at different stages.

As a major approach for controlling electromagnetic (EM) waves, metamaterials have experienced an abundant and rapid development in the 21st century. They have provided flexible and powerful techniques for controlling EM waves and brought many unique applications that are difficult to realise with natural materials. With increasing demands on dynamic controls of the EM waves, many innovations have been conducted in both three-dimensional metamaterials and two-dimensional metasurfaces, in which the meta-atom has been gradually evolved from passive to active. In 2014, coding and digital mechanisms were initially introduced to the metamaterials, further advancing the appearance of digitally programmable metamaterials. The programmable metamaterials have shown great potentials in not only real-time manipulations of the EM waves, but also direct information processing on the EM wave level. In this article, we present an in-depth review of the programmable EM metamaterials and metasurfaces, focusing on the programmable features including theoretical concepts, implementing methods and applications in EM controls. We first give a short retrospect of traditional metamaterials and metasurfaces, followed by the concepts and detailed discussions of digital coding and field-programmable metamaterials. Then, we introduce space-domain, time-domain and space–time-domain programmable metamaterials and metasurfaces, mainly focusing on their theories, functionalities, experimental implementations, and system-level applications. Finally, we conclude the current advances of the programmable metamaterials and metasurfaces, and give a prospect for the future developments.

When conducting accident analysis, the assessment of risk is one of the important links. Moreover, with regards to crew training, risk cognition is also an important training subject. However, most of the existing researches only rely on a single or a few data sources. It is necessary to fuse the collected multi-source data to obtain a more comprehensive risk evaluation model. There are few studies on the three-dimensional (3D) multi-modal data-fusion-based trajectory risk cognition. In this paper, a fuzzy logic-based trajectory risk cognition method is proposed based on multi-model spatial data fusion and accident data mining. First, the necessity of multi-model spatial data fusion is analysed and a data-fusion-based scene map is constructed. Second, a risk cognition model fused by multiple factors, multi-dimensional spatial calculations as well as data mining results is proposed, including a novel ship boundary calculation approach and newly constructed factors. Finally, a radar chart is used to illustrate the risk, and a risk cognition system is developed. Experiment results confirm the effectiveness of the method. It can be applied to train human operators of unmanned ship systems.

From geotechnical applications to space exploration, auger drilling is often used as a standard tool for soil sample collection, instrument installation and others. Focusing on granular flow associated with the rotary drilling process, we investigate the performance of auger drilling in terms of sampling efficiency, defined as the mass ratio of the soil sample collected in the coring tube to its total volume at a given penetration depth, by means of experiments, numerical simulations as well as theoretical analysis. The ratio of rotation to penetration speed is found to play a crucial role in the sampling process. A continuum model for the coupled granular flow in both coring and discharging channels is proposed to elucidate the physical mechanism behind the sampling process. Supported by a comparison with experimental results, the continuum model provides a practical way to predict the performance of auger drilling. Further analysis reveals that the drilling process approaches a steady state with constant granular flow speeds in both channels. In the steady state, sampling efficiency decreases linearly with the growth of the rotation to penetration speed ratio, which can be well captured by the analytical solution of the model. The analytical solution also suggests that the sampling efficiency is independent of gravity in the steady state, which has profound implications for extraterrestrial sample collection in future space missions.

The aim of this study was to investigate the association between daily Se intake and postpartum weight retention (PPWR) among Chinese lactating women, and the impact of their Se nutritional status on infants’ physical development. Se contents in breast milk and plasma collected from 264 lactating Chinese women at the 42nd day postpartum were analysed with inductively coupled plasma MS. Daily Se intake was calculated based on plasma Se concentration. The dietary data of 24-h records on three consecutive days were collected. Infant growth status was evaluated with WHO standards by Z-scores. Linear regression analyses and multinomial logistic regression were conducted to examine the impact of Se disequilibrium (including other factors) on PPWR and growth of infants, respectively. The results indicated that: (1) the daily Se intake of the subjects was negatively associated with their PPWR (B = −0·002, 95 % CI − 0·003, 0·000, P = 0·039); (2) both insufficient Se daily intake (B = −0·001, OR 0·999, 95 % CI 0·998, 1·000, P = 0·014) and low level of Se in milk (B = −0·025, OR 0·975, 95 % CI 0·951, 0·999, P = 0·021) had potential associations with their infants’ wasting, and low level of Se in milk (B = −0·159, OR 0·853, 95 % CI 0·743, 0·980, P = 0·024) had a significant association with their infants’ overweight. In conclusion, the insufficient Se nutritional status of lactating Chinese women was first found as one possible influencing factor of their PPWR as well as low physical development of their offspring.

A flexible surface-enhanced Raman scattering (SERS) substrate was prepared by vacuum evaporation of silver on the surface of woven nylon fabrics. SERS properties of the Ag-coated nylon fabrics varied as the thickness of silver coatings changed, relative to the morphologies and distribution of silver nanoparticles (NPs) on fiber. The SERS enhancement performance of Ag-coated nylon fabrics was evaluated by collecting Raman signals of different concentrations of p-aminothiophenol (PATP). The results suggested that the nylon fabrics coated with 10 nm thickness Ag NPs coatings possessed high SERS activity and its detection concentration for PATP is as low as 10−9 M. Furthermore, sensitive SERS signals with excellent reproducibility (Relative standard deviation = 8.25%) and stability (30 days) have been demonstrated. In addition, the SERS nylon fabrics have been applied to rapidly detect thiram pesticides on cucumber, which indicated a great potential for trace analysis.

In late December 2019, patients of atypical pneumonia due to an unidentified microbial agent were reported in Wuhan, Hubei Province, China. Subsequently, a novel coronavirus was identified as the causative pathogen which was named SARS-CoV-2. As of 12 February 2020, more than 44 000 cases of SARS-CoV-2 infection have been confirmed in China and continue to expand. Provinces, municipalities and autonomous regions of China have launched first-level response to major public health emergencies one after another from 23 January 2020, which means restricting movement of people among provinces, municipalities and autonomous regions. The aim of this study was to explore the correlation between the migration scale index and the number of confirmed coronavirus disease 2019 (COVID-19) cases and to depict the effect of restricting population movement. In this study, Excel 2010 was used to demonstrate the temporal distribution at the day level and SPSS 23.0 was used to analyse the correlation between the migration scale index and the number of confirmed COVID-19 cases. We found that since 23 January 2020, Wuhan migration scale index has dropped significantly and since 26 January 2020, Hubei province migration scale index has dropped significantly. New confirmed COVID-19 cases per day in China except for Wuhan gradually increased since 24 January 2020, and showed a downward trend from 6 February 2020. New confirmed COVID-19 cases per day in China except for Hubei province gradually increased since 24 January 2020, and maintained at a high level from 24 January 2020 to 4 February 2020, then showed a downward trend. Wuhan migration scale index from 9 January to 22 January, 10 January to 23 January and 11 January to 24 January was correlated with the number of new confirmed COVID-19 cases per day in China except for Wuhan from 22 January to 4 February. Hubei province migration scale index from 10 January to 23 January and 11 January to 24 January was correlated with the number of new confirmed COVID-19 cases per day in China except for Hubei province from 22 January to 4 February. Our findings suggested that people who left Wuhan from 9 January to 22 January, and those who left Hubei province from 10 January to 24 January, led to the outbreak in the rest of China. The ‘Wuhan lockdown’ and the launching of the first-level response to this major public health emergency may have had a good effect on controlling the COVID-19 epidemic. Although new COVID-19 cases continued to be confirmed in China outside Wuhan and Hubei provinces, in our opinion, these are second-generation cases.