A dual-band dual-polarized wearable antenna that applies to two different operating modes of wireless body area networks is proposed in this letter. The antenna radiates simultaneously in the ISM band at 2.45 and 5.8 GHz. It consists of a rigid button-like radiator and a flexible fabric radiator. At 2.45 GHz, an omnidirectional circularly polarized pattern is radiated by the flexible radiator, which is suitable for the on-body communication. At the same time, a linearly polarized broadside pattern for off-body communication is generated by button radiator at 5.8 GHz. The antenna has been validated in free space and human body environments. The impedance bandwidth at 2.45 and 5.8 GHz are 5% and 35%, and the gain is measured to be 0.15 and 5.95 dBi, respectively. Furthermore, the specific absorption rates are simulated. At 2.45 and 5.8 GHz, the results averaged over 1 g of body tissue are 0.128 and 0.055 W/kg. The maximum value at both bands is below the IEEE C95.3 standard of 1.6 W/kg.

The incorporation of trace metals into land snail shells may record the ambient environmental conditions, yet this potential remains largely unexplored. In this study, we analyzed modern snail shells (Cathaica sp.) collected from 16 sites across the Chinese Loess Plateau to investigate their trace metal compositions. Our results show that both the Sr/Ca and Ba/Ca ratios exhibit minimal intra-shell variability and small inter-shell variability at individual sites. A significant positive correlation is observed between the shell Sr/Ca and Ba/Ca ratios across the plateau, with higher values being recorded in the northwestern sites where less monsoonal rainfall is received. We propose that shell Sr/Ca and Ba/Ca ratios, which record the composition of soil solution, may be controlled by the Rayleigh distillation in response to prior calcite precipitation. Higher rainfall amounts may lead to a lower degree of Rayleigh distillation and thus lower shell Sr/Ca and Ba/Ca ratios. This is supported by the distinct negative correlation between summer precipitation and shell Sr/Ca and Ba/Ca ratios, enabling us to reconstruct summer precipitation amounts using the Sr/Ca and Ba/Ca ratios of Cathaica sp. shells. The potential application of these novel proxies may also be promising for other terrestrial mollusks living in the loess deposits globally.

The nucleation of bubbles on rough substrates has been widely investigated in various applications such as electrolysis processes and fluid transportation in pipelines. However, the microscopic mechanisms underlying surface bubble nucleation are not fully understood. Using molecular dynamics simulations, we evaluate the probability of surface bubble nucleation, quantified by the magnitude of the nucleation threshold. Bubble nucleation preferentially occurs at the solid interfaces containing nanoscale defects or wells (nanowells), where reduced nucleation thresholds are observed. For the gas-entrapped nanowell, as the nanowell width decreases, the threshold of bubble nucleation around the nanowell gradually increases, eventually approaching a critical value close to that of a smooth surface. This results from a decrease in the amount of entrapped gas that promotes bubble nucleation, and the entrapped gas eventually converges to a critical state as the width decreases. For the liquid-filled nanowell, bubble nucleation initiates from the inner corner of the large nanowell. As the nanowell width decreases, the threshold is first kept constant and then decreases. This results from a decrease in the amount of filled liquid that inhibits bubble nucleation and from the enhanced confinement effect of the inner wall on the filled liquid as the width decreases. In this work, we propose a multiscale model integrating classical nucleation theory, van der Waals fluid theory and statistical mechanics to describe the relationship between nucleation threshold and nanowell width. Eventually, a unified phase diagram of bubble nucleation at the rough interface is summarised, offering fundamental insights for integrated system design.

Cavitation bubble pulsation and liquid jet loads are the main causes of hydraulic machinery erosion. Methods to weaken the load influences have always been hot topics of related research. In this work, a method of attaching a viscous layer to a rigid wall is investigated in order to reduce cavitation pulsations and liquid jet loads, using both numerical simulations and experiments. A multiphase flow model incorporating viscous effects has been developed using the Eulerian finite element method (EFEM), and experimental methods of a laser-induced bubble near the viscous layer attached on a rigid wall have been carefully designed. The effects of the initial bubble–wall distance, the thickness of the viscous layer, and the viscosity on bubble pulsation, migration and wall pressure load are investigated. The results show that the bubble migration distance, the normalised thickness of the oil layer and the wall load generally decrease with the initial bubble–wall distance or the oil-layer parameters. Quantitative analysis reveals that when the initial bubble–wall distance remains unchanged, there exists a demarcation line for the comparison of the bubble period and the reference period (the bubble period without viscous layer under the same initial bubble–wall distance), and a logarithmic relationship is observed that $\delta \propto \log_{10} \mu ^*$, where $\delta =h/R_{max}$ is the thickness of the viscous layer h normalised by the maximum bubble radius $R_{max}$, $\mu ^* = \mu /({R_{max }}\sqrt {{\rho }{{\mathop {P}\nolimits } _{{atm}}}})$ is the dynamic viscosity $\mu$ normalised by water density $ \rho $ and atmospheric pressure $P_{atm}$. The results of this paper can provide technical support for related studies of hydraulic cavitation erosion.

A theoretical framework has been established to investigate the modulational instability of electromagnetic waves in magnetized electron–positron plasmas. The framework is capable of analyzing electromagnetic waves of any intensity and plasmas at any temperature. A fully relativistic hydrodynamic model, incorporating relativistic velocities and thermal effects, is used to describe the relativistic dynamics of particles in plasmas. Under the weakly magnetized approximation, a modified nonlinear Schrödinger equation, governing the dynamics of the envelope of electromagnetic waves in plasmas, is obtained. The growth rate of the modulational instability is then given both theoretically and numerically. By analyzing the dependence of the growth rate on some key physical parameters, the coupled interplay of relativistic effects, ponderomotive forces, thermal effects and magnetic fields on electromagnetic waves can be clarified. The findings demonstrate that specific combinations of physical parameters can significantly enhance modulational instability, providing a theoretical basis for controlling the propagation of electromagnetic waves in plasmas. This framework has broad applicability to most current laser–plasma experiments and high-energy radiation phenomena from stellar surfaces.

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by inattention and/or hyperactivity-impulsivity, accompanied by deficits in executive function (EF). However, how the two core symptoms of ADHD are affected by EF deficits remains unclear. 649 children with ADHD were recruited. Data were collected from ADHD rating scales, the Behavior Rating Inventory of EF (BRIEF), and other demographic questionnaires. Regression and path analyses were conducted to explore how deficits in cool and hot EF influence different ADHD core symptoms. Latent class analysis and logistic regression were employed to further examine whether classification of ADHD subtypes is associated with specific EF deficits. EF deficits significantly predicted the severity of ADHD core symptoms, with cool EF being a greater predictor of inattention and hot EF having a more significant effect on hyperactivity/impulsivity. Moreover, person-centered analyses revealed higher EF deficits in subtypes of ADHD with more severe symptoms, and both cool and hot EF deficits could predict the classification of ADHD subtypes. Our findings identify distinct roles for cool and hot EF deficits in the two core symptoms of ADHD, which provide scientific support for the development of ADHD diagnostic tools and personalized intervention from the perspective of specific EF deficits.

An actively controllable cascaded proton acceleration driven by a separate 0.8 picosecond (ps) laser is demonstrated in proof-of-principle experiments. MeV protons, initially driven by a femtosecond laser, are further accelerated and focused into a dot structure by an electromagnetic pulse (EMP) on the solenoid, which can be tuned into a ring structure by increasing the ps laser energy. An electrodynamics model is carried out to explain the experimental results and show that the dot-structured proton beam is formed when the outer part of the incident proton beam is optimally focused by the EMP force on the solenoid; otherwise, it is overfocused into a ring structure by a larger EMP. Such a separately controlled mechanism allows precise tuning of the proton beam structures for various applications, such as edge-enhanced proton radiography, proton therapy and pre-injection in traditional accelerators.

This paper investigates both conditional and unconditional convergence in labor productivity within the manufacturing industries of the Eurozone over the period 1963 – 2018. We employ two innovative models: constant and varying-coefficient hierarchical panel data convergence regression models, each equipped with two sets of latent factor structures—one comprising global factors and the other industry-specific factors. These models offer distinct advantages, allowing for both global and industry-specific cross-sectional dependencies and permitting parameter heterogeneity across individual industries. Our findings reveal both conditional and unconditional convergence across the manufacturing industry as a whole, as well as among the majority of the 23 sub-manufacturing industries at the ISIC two-digit level. Moreover, we observe significant variation in convergence dynamics among these sub-manufacturing industries. Robustness checks, performed across different subperiods, confirm the reliability of our results. Furthermore, a comparison of our model’s outcomes with those of two alternative models provides additional support for our conclusions.

This paper presents a millimeter-wave end-fire dual-polarized (DP) array antenna with symmetrical radiation patterns and high isolation. The DP radiation element is formed by integrating a quasi-Yagi antenna (providing horizontal polarization) into a pyramidal horn antenna (providing vertical polarization), resulting in a DP radiation element with a symmetrical radiation aperture. To efficiently feed the DP element while maintaining high isolation, a mode-composite full-corporate-feed network is employed, comprising substrate-integrated waveguide supporting the TE10 mode and substrate-integrated coaxial line supporting the TEM mode. This design eliminates the need for additional transition structures, achieving excellent mode isolation and a reduced substrate layer number. A 1 × 4-element DP array prototype operating at 26.5–29.5 GHz using low temperature co-fired ceramic technology was designed, fabricated, and measured. The test results indicate that the prototype achieves an average gain exceeding 10 dBi for both polarizations within the operating band. Thanks to the symmetrical DP radiation element and mode-composite full-corporate-feed network, symmetrical radiation patterns for both polarizations are observed in both the horizontal and vertical planes, along with a high cross-polarization discrimination of 22 dB and polarization port isolation of 35 dB.

The oriental armyworm, Mythimna separata (Walker), is a highly migratory pest known for its sudden larval outbreaks, which result in severe crop losses. These unpredictable surges pose significant challenges for timely and accurate monitoring, as conventional methods are labour-intensive and prone to errors. To address these limitations, this study investigates the use of machine learning for automated and precise identification of M. separata larval instars. A total of 1577 larval images representing different instar were analysed for geometric, colour, and texture features. Additionally, larval weight was predicted using 13 regression models. Instar identification was conducted using Support Vector Classifier (SVC), Random Forest, and Multi-Layer Perceptron. Key feature contributing to classification accuracy were subsequently identified through permutation feature importance analysis. The results demonstrated the potential of machine learning for automating instar identification with high efficiency and accuracy. Predicted larval weight emerged as a key feature, significantly enhancing the performance of all identification models. Among the tested approaches, BaggingRegressor exhibited the best performance for larval weight prediction (R2 = 98.20%, RMSE = 0.2313), while SVC achieved the highest instar identification accuracy (94%). Overall, the integration of larval weight with other image-derived features proved to be a highly effective strategy. This study demonstrates the efficacy of machine learning in enhancing pest monitoring systems by providing a scalable and reliable framework for precise pest management. The proposed methodology significantly improves larval instar identification accuracy and efficiency, offering actionable insights for implementing targeted biological and chemical control strategies.

Land use change has significantly altered most ecosystem functioning, such as nutrition provisioning, water flows and pollination services. So far, the impact of land use change on the dietary diversity of predatory insects has remained largely unexplored. In this study, we explored the prey composition of reared yellow-legged hornets Vespa velutina Lepeletier (Hymenoptera: Vespidae) in landscapes with a gradient of surrounding green lands, using metabarcoding of feces eliminated by larvae. The hornets primarily fed upon insects, with dipterans, coleopterans, lepidopterans, hemipterans, hymenopterans, and orthopterans being the dominant prey groups. The percentage of green lands had a significantly positive effect on prey richness at a spatial scale of 1500 m, but no effect on Shannnon index of the prey community. Meanwhile, the green lands had significantly positive effects on richness of coleopteran prey and lepidopteran prey, but no significant effect on richness of dipteran prey, hemipteran prey, hymenopteran prey, or orthopteran prey. In terms of beta diversity, the percentage of green lands explained the dissimilarity of prey communities among landscapes, whereas local factors, such as the distance to green lands and the distance to buildings, did not explain the dissimilarity. Our study indicated that the green lands in the landscape positively affected the dietary diversity of reared yellow-legged hornets, but this effect varied among different taxonomic groups of prey.

The greatest challenge in pressure reconstruction from the measured velocity fields is that the error of material acceleration is significantly contaminated due to error propagation. Particularly for flows with moving boundaries, accurate boundary velocities are difficult to obtain due to error propagation, and a complex boundary processing technique is needed to treat the moving boundaries. The present work proposes a machine-learning-based method to determine the pressure for incompressible flows with moving boundaries. The proposed network consists of two neural networks: one network, named the boundary network, is used to track the Lagrangian boundary points; the other physics-informed neural network, named the flow network, is adopted to approximate the flow fields. These two networks are coupled by imposing boundary conditions. We further propose a new dynamic weight strategy for the loss terms to guarantee convergence and stability. The performance of the proposed method is validated by two examples: the flow over an oscillating cylinder and the flow around a swimming fish. The proposed method can accurately determine the pressure fields and boundary motion from synthetic particle image velocimetry (PIV) flow fields. Moreover, this method can also predict the boundary and pressure at a given instant without supervised data. Finally, this method was applied to reconstruct the pressure from the two-dimensional and three-dimensional PIV velocities of the left ventricle. All of the results indicate that the proposed method can accurately reconstruct the pressure fields for flows with moving boundaries and is a novel method for surface pressure estimation.

The sulphur microbial diet (SMD), a dietary pattern associated with forty-three sulphur-metabolising bacteria, may influence gut microbiota composition and contribute to ageing process through gut-produced hydrogen sulfide (H2S). We aimed to explore the association between SMD and biological age (BA) acceleration, using the cross-sectional study that included 71 579 individuals from the UK Biobank. The SMD score was calculated by multiplying β-coefficients by corresponding serving sizes and summing them, based on dietary data collected using the Oxford WebQ, a 24-hour dietary assessment tool. BA was assessed using Klemerae–Doubal (KDM) and PhenoAge methods. The difference between BA and chronological age refers to the age acceleration (AgeAccel), termed ‘KDMAccel’ and ‘PhenoAgeAccel’. Generalised linear regression was performed. Mediation analyses were used to investigate underlying mediators including BMI and serum aspartate aminotransferase/alanine aminotransferase (AST/ALT) ratio. Following adjustment for multiple variables, a positive association was observed between consuming a dietary pattern with a higher SMD score and both KDMAccel (βQ4 v. Q1 = 0·35, 95 % CI = 0·27, 0·44, P < 0·001) and PhenoAgeAccel (βQ4 v. Q1 = 0·32, 95 % CI = 0·23, 0·41, P < 0·001). Each 1-SD increase in SMD score was positively associated with the acceleration of BA by 7·90 % for KDMAccel (P < 0·001) and 7·80 % for PhenoAgeAccel (P < 0·001). BMI and AST/ALT mediated the association. The stratified analysis revealed stronger accelerated ageing impacts in males and smokers. Our study indicated a higher SMD score is associated with elevated markers of biological ageing, supporting the potential utility of gut microbiota-targeted dietary interventions in attenuating the ageing process.

Rogue waves (RWs) can form on the ocean surface due to the well-known quasi-four-wave resonant interaction or superposition principle. The first is known as the nonlinear focusing mechanism and leads to an increased probability of RWs when unidirectionality and narrowband energy of the wave field are satisfied. This work delves into the dynamics of extreme wave focusing in crossing seas, revealing a distinct type of nonlinear RWs, characterised by a decisive longevity compared with those generated by the dispersive focusing (superposition) mechanism. In fact, through fully nonlinear hydrodynamic numerical simulations, we show that the interactions between two crossing unidirectional wave beams can trigger fully localised and robust development of RWs. These coherent structures, characterised by a typical spectral broadening then spreading in the form of dual bimodality and recurrent wave group focusing, not only defy the weakening expectation of quasi-four-wave resonant interaction in directionally spreading wave fields, but also differ from classical focusing mechanisms already mentioned. This has been determined following a rigorous lifespan-based statistical analysis of extreme wave events in our fully nonlinear simulations. Utilising the coupled nonlinear Schrödinger framework, we also show that such intrinsic focusing dynamics can be captured by weakly nonlinear wave evolution equations. This opens new research avenues for further explorations of these complex and intriguing wave phenomena in hydrodynamics as well as other nonlinear and dispersive multi-wave systems.

This paper provides an overview of the current status of ultrafast and ultra-intense lasers with peak powers exceeding 100 TW and examines the research activities in high-energy-density physics within China. Currently, 10 high-intensity lasers with powers over 100 TW are operational, and about 10 additional lasers are being constructed at various institutes and universities. These facilities operate either independently or are combined with one another, thereby offering substantial support for both Chinese and international research and development efforts in high-energy-density physics.

Machine learning has already shown promising potential in tiled-aperture coherent beam combining (CBC) to achieve versatile advanced applications. By sampling the spatially separated laser array before the combiner and detuning the optical path delays, deep learning techniques are incorporated into filled-aperture CBC to achieve single-step phase control. The neural network is trained with far-field diffractive patterns at the defocus plane to establish one-to-one phase-intensity mapping, and the phase prediction accuracy is significantly enhanced thanks to the strategies of sin-cos loss function and two-layer output of the phase vector that are adopted to resolve the phase discontinuity issue. The results indicate that the trained network can predict phases with improved accuracy, and phase-locking of nine-channel filled-aperture CBC has been numerically demonstrated in a single step with a residual phase of λ/70. To the best of our knowledge, this is the first time that machine learning has been made feasible in filled-aperture CBC laser systems.