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.

Resilient enterprises thrive under adverse conditions given their preparedness for crises. This study proposes that executives’ vigilant managerial cognition is essential for enhancing enterprise resilience. To measure this cognition, the study developed a textual index using machine learning methods and analyzed a sample of Chinese enterprises to assess the impact of executives’ vigilant managerial cognition on enterprise resilience. The findings indicate that this cognition is positively related to enterprise resilience, where the relationship is stronger in enterprises with robust internal controls. The primary contribution of this study is the conceptualization of vigilant managerial cognition and its established positive relationship with enterprise resilience. Furthermore, by introducing a novel quantitative measure of managerial cognition through textual analysis and machine learning, the study paves the way for future research on managerial cognition within firms.

Ultra-thin liquid sheets generated by impinging two liquid jets are crucial high-repetition-rate targets for laser ion acceleration and ultra-fast physics, and serve widely as barrier-free samples for structural biochemistry. The impact of liquid viscosity on sheet thickness should be comprehended fully to exploit its potential. Here, we demonstrate experimentally that viscosity significantly influences thickness distribution, while surface tension primarily governs shape. We propose a thickness model based on momentum exchange and mass transport within the radial flow, which agrees well with the experiments. These results provide deeper insights into the behaviour of liquid sheets and enable accurate thickness control for various applications, including atomization nozzles and laser-driven particle sources.

This paper introduces a distributed online learning coverage control algorithm based on sparse Gaussian process regression for addressing the problem of multi-robot area coverage and source localization in unknown environments. Considering the limitations of traditional Gaussian process regression in handling large datasets, this study employs multiple robots to explore the task area to gather environmental information and approximate the posterior distribution of the model using variational free energy methods, which serves as the input for the centroid Voronoi tessellation algorithm. Additionally, taking into consideration the localization errors, and the impact of obstacles, buffer factors and centroid Voronoi tessellation algorithms with separating hyperplanes are introduced for dynamic robot task area planning, ultimately achieving autonomous online decision-making and optimal coverage. Simulation results demonstrate that the proposed algorithm ensures the safety of multi-robot formations, exhibits higher iteration speed, and improves source localization accuracy, highlighting the effectiveness of model enhancements.

Little is known about the association between iodine nutrition status and bone health. The present study aimed to explore the connection between iodine nutrition status, bone metabolism parameters, and bone disease (osteopenia and osteoporosis). A cross-sectional survey was conducted involving 391, 395, and 421 adults from iodine fortification areas (IFA), iodine adequate areas (IAA), and iodine excess areas (IEA) of China. Iodine nutrition status, bone metabolism parameters and BMD were measured. Our results showed that, in IEA, the urine iodine concentrations (UIC) and serum iodine concentrations (SIC) were significantly higher than in IAA. BMD and Ca2+ levels were significantly different under different iodine nutrition levels and the BMD were negatively correlated with UIC and SIC. Univariate linear regression showed that gender, age, BMI, menopausal status, smoking status, alcohol consumption, UIC, SIC, free thyroxine, TSH, and alkaline phosphatase were associated with BMD. The prevalence of osteopenia was significantly increased in IEA, UIC ≥ 300 µg/l and SIC > 90 µg/l groups. UIC ≥ 300 µg/l and SIC > 90 µg/l were risk factors for BMD T value < –1·0 sd. In conclusion, excess iodine can not only lead to changes in bone metabolism parameters and BMD, but is also a risk factor for osteopenia and osteoporosis.

In this paper, we propose a hybrid sparse array design utilizing Delaunay Triangulation algorithm for element positioning and Convex algorithm for element excitation optimization. This Delaunay Triangulation algorithm yields a radiation pattern devoid of grating lobes. Then Convex algorithm is used to optimize the element excitations to further decrease side-lobe-level. The minimum inter-element distance is as large as 8 times of wavelength. The peak-side-lobe-level can be −17.3 dB. Furthermore, beam steering can be achieved with good performance within 80° field-of-view range.

Sensory neuron membrane protein (SNMP) gene play a crucial role in insect chemosensory systems. However, the role of SNMP in the host searching behaviour of Rhopalosiphum padi (Hemiptera: Aphididae), a highly destructive pest of cereal crops, has not been clearly understood. Our previous research has shown that three wheat volatile organic compounds (VOCs) – (E)-2-hexenol, linalool, and octanal can attract R. padi, but the involvement of SNMP in the aphid’s olfactory response to these wheat VOCs has not to be elucidated. In this study, only one SNMP gene was cloned and characterised from R. padi. The results revealed that the SNMP belongs to the SNMP1 subfamily and was named RpadSNMP1. RpadSNMP11 was predominantly expressed in the antennae of the aphid, with significantly higher expression levels observed in winged forms, indicating that it is involved in olfactory responses of R. padi. RpadSNMP1 expression was significantly up-regulated following starvation, and the expression of this gene showed a decreasing trend after 24 h of aphid feeding. Functional analysis through RpadSNMP1 knockdown demonstrated a significant decrease in R. padi’s ability to search for host plants. The residence time of R. padi injected with dsRpadSNMP1 significantly shortened in response to (E)-2-hexenol, linalool and octanal according to the four-arm olfactometer, indicating the crucial role of RpadSNMP1 in mediating the aphid’s response to these wheat VOCs. Molecular docking suggested potential binding interactions between RpadSNMP1 and three wheat VOCs. Overall, these findings provided evidence for the involvement of RpadSNMP1 in host plant searching and lay a foundation for developing new methods to control this destructive pest.

This paper presents a notched ultra-wideband antenna designed to suppress interference from narrowband communication systems. The antenna features a defected ground structure and a stepped microstrip feedline for improved impedance matching and enhanced bandwidth. A bent slot structure is incorporated into the radiating patch to achieve the band-notched characteristic. It has a wide tunable frequency range which allows for flexible adjustment of the notch frequency. Traditional optimization methods, such as numerical analysis, are computationally expensive and inefficient, while heuristic algorithms are less precise. To address these challenges, an improved one-dimensional convolutional neural network (1DCNN-IPS) model is proposed for optimizing the bent slot design more efficiently. The trained 1DCNN-IPS model can accurately predict the antenna’s electromagnetic parameters, reducing mean squared error and training times compared to traditional methods. This provides an efficient and precise solution for antenna structural optimization.

Green water loads on prismatic obstacles (representing topside structures) mounted on the raised deck of a simplified vessel are investigated using computational fluid dynamics simulations and physical model testing with emphasis on examining different structure shapes, orientation angles and relative structure size. For each scenario investigated, several flow features are identified that characterize the green water interaction with the structure and influence loads, namely delayed flow diversion, formation of a vertical jet, scattered wave formation and the development of complex wake patterns. Comparing across structures, these interactions are more pronounced for blunt objects, and the associated force impulse is larger. For example, a cube with flow at normal incidence is found to experience approximately twice the force impulse of a circular cylinder of the same projected area. Equally, rotation of the cube leads to reduced run-up height and streamwise force on the structure. To explain these trends, a theoretical model based on Newtonian flow theory is adopted. This model provides an estimate of the streamwise force exerted on obstacles in high-Froude-number flows and shows good agreement with the numerical results when the flow is supercritical, shallow (small water depth relative to structure width) and the structure is tall (large structure height relative to water depth). Despite some limitations, the model should provide an efficient force prediction tool for practical use in design.

We incorporate the liquidity trap and private behavioral preferences into a New Keynesian dynamic stochastic general equilibrium model to analyze fiscal multipliers. The results indicate that the influence of the liquidity trap on fiscal policy is driven by a combination of the interest rate transmission effect and the precautionary savings effect, showing a notable amplification of multipliers based on estimates from U.S. data. Furthermore, we examine two types of private behavioral preferences: habit formation and investor confidence. Habit formation significantly boosts short-term government spending multipliers while exhibiting diverse impacts on different types of taxation. Compared to superficial habits, deep habits result in flatter multiplier curves. Investor confidence, being highly sensitive to output fluctuations, enhances both spending and tax multipliers over the medium to long term. Additionally, the investor confidence channel slightly amplifies the expansionary effect of the liquidity trap on multipliers, contrasting with the impact of habit formation.

The paper presents a novel control method aimed at enhancing the trajectory tracking accuracy of two-link mechanical systems, particularly nonlinear systems that incorporate uncertainties such as time-varying parameters and external disturbances. Leveraging the Udwadia–Kalaba equation, the algorithm employs the desired system trajectory as a servo constraint. First, the system’s constraints to construct its dynamic equation and apply generalized constraints from the constraint equation to an unconstrained system. Second, we design a robust approximate constraint tracking controller for manipulator control and establish its stability using Lyapunov’s law. Finally, we numerically simulate and experimentally validate the controller on a collaborative platform using model-based design methods.

Foodborne diseases are ongoing and significant public health concerns. This study analysed data obtained from the Foodborne Outbreaks Surveillance System of Wenzhou to comprehensively summarise the characteristics of foodborne outbreaks from 2012 to 2022. A total of 198 outbreaks were reported, resulting in 2,216 cases, 208 hospitalisations, and eight deaths over 11 years. The findings suggested that foodborne outbreaks were more prevalent in the third quarter, with most cases occurring in households (30.8%). Outbreaks were primarily associated with aquatic products (17.7%) as sources of contamination. The primary transmission pathways were accidental ingestion (20.2%) and multi-pathway transmission (12.1%). Microbiological aetiologies (46.0%), including Vibrio parahaemolyticus, Salmonella ssp., and Staphylococcus aureus, were identified as the main causes of foodborne outbreaks. Furthermore, mushroom toxins (75.0%), poisonous animals (12.5%), and poisonous plants (12.5%) were responsible for deaths from accidental ingestion. This study identified crucial settings and aetiologies that require the attention of both individuals and governments, thereby enabling the development of effective preventive measures to mitigate foodborne outbreaks, particularly in coastal cities.