This study explored the relationship between multifaceted multilingualism and cognitive shifting through a task-switching paradigm using fMRI. Multilingualism was modeled from both convergent (i.e., integrated multilingual index) and divergent (i.e., L2 proficiency, interpreting training, language entropy) perspectives. Participants identified letters or numbers based on task cues, with Repeat trials maintaining the same task and Switch trials requiring a different task. Switch cost (Switch–Repeat) was used to reflect shifting demands. Better task-switching performance was associated with a higher integrated multilingual index and interpreting training. Neuroimaging indicated that multilinguals predominantly engaged left-hemisphere regions for switching, with extensive multilingual experience requiring fewer neural resources for switch cost (i.e., more efficient processing for cognitive control). During task switching, brain connectivity was regulated locally by L2 proficiency, and globally by interpreting training. These findings underscore the importance of considering multifaceted multilingual experience to understand its impact on cognitive function and brain activity.

The safety of human-collaborative operations with robots depends on monitoring the external torque of the robot, in which there are toque sensor-based and torque sensor-free methods. Economically, the classic method for estimating joint external torque is the first-order momentum observer (MOB) based on a physic model without torque sensors. However, uncertainties in the dynamic model, which encompasses parameters identification error and joint friction, affect the torque estimation accuracy. To address this issue, this paper proposes using the backpropagation neural network (BPNN) method to estimate joint external torque without the delicate physical model by utilizing the powerful machine learning ability to handle the uncertainties of the MOB method and improve the accuracy of torque estimation. Using data obtained from the torque sensor to train the BPNN to build up a digital torque model, the trained BPNN can perceive force in practical applications without relying on the torque sensor. In the end, by contrast to the classic first-order MOB, the result demonstrates that BPNN achieves higher estimation accuracy compared to the MOB.

Substantial changes resulting from the interaction of environmental and dietary factors contribute to an increased risk of obesity, while their specific associations with obesity remain unclear. Identify inflammation-related dietary patterns (DPs) and explore their associations with obesity among urbanized Tibetan adults under significant environmental and dietary changes.Totally, 1826 subjects from the suburbs of Golmud City were enrolled in an open cohort study, of which 514 were followed up. Height, weight, and waist circumference were used to define overweight and obesity. DPs were derived using reduced rank regression with 41 food groups as predictors and hs-CRP and prognostic nutritional index as inflammatory response variables. Altitude was classified as high or ultra-high. Two DPs were extracted. DP-1 was characterized by having high consumptions of sugar-sweetened beverages, savory snacks, and poultry, and a low intake of tsamba. DP-2 had high intakes of poultry, pork, animal offal, and fruits, and a low intake of butter tea. Participants in the highest tertiles (T3) of DPs had increased risks of overweight and obesity (DP-1: OR=1.37, 95% CI: 1.07, 1.77; DP-2: OR=1.48, 95% CI: 1.18, 1.85) than those in the lowest tertiles (T1). Participants in T3 of DP-2 had an increased risk of central obesity (OR=2.25, 95% CI: 1.49, 3.39) than those in T1. The positive association of DP-1 with overweight and obesity was only significant at high altitudes, while no similar effect was observed for DP-2. Inflammation-related DPs were associated with increased risks of overweight and/or obesity.

The presence of dispersed-phase droplets can result in a notable increase in a system's drag. However, our understanding of the mechanism underlying this phenomenon remains limited. In this study, we use three-dimensional direct numerical simulations with a modified multi-marker volume-of-fluid method to investigate liquid–liquid two-phase turbulence in a Taylor–Couette geometry. The dispersed phase has the same density and viscosity as the continuous phase. The Reynolds number $Re\equiv r_i\omega _i d/\nu$ is fixed at 5200, the volume fraction of the dispersed phase is up to $40\,\%$, and the Weber number $We\equiv \rho u^2_\tau d/\sigma$ is approximately 8. It is found that the increase in the system's drag originates from the contribution of interfacial tension. Specifically, droplets experience significant deformation and stretching in the streamwise direction due to shear near the inner cylinder. Consequently, the rear end of the droplets lags behind the fore head. This causes opposing interfacial tension effects on the fore head and rear end of the droplets. For the fore head of the droplets, the effect of interfacial tension appears to act against the flow direction. For the rear end, the effect appears to act in the flow direction. The increase in the system's drag is attributed primarily to the effect of interfacial tension on the fore head of the droplets which leads to the hindering effect of the droplets on the surrounding continuous phase. This hindering effect disrupts the formation of high-speed streaks, favouring the formation of low-speed ones, which are generally associated with higher viscous stress and drag of the system. This study provides new insights into the mechanism of drag enhancement reported in our previous experiments.

Developing large-eddy simulation (LES) wall models for separated flows is challenging. We propose to leverage the significance of separated flow data, for which existing theories are not applicable, and the existing knowledge of wall-bounded flows (such as the law of the wall) along with embedded learning to address this issue. The proposed so-called features-embedded-learning (FEL) wall model comprises two submodels: one for predicting the wall shear stress and another for calculating the eddy viscosity at the first off-wall grid nodes. We train the former using the wall-resolved LES (WRLES) data of the periodic hill flow and the law of the wall. For the latter, we propose a modified mixing length model, with the model coefficient trained using the ensemble Kalman method. The proposed FEL model is assessed using the separated flows with different flow configurations, grid resolutions and Reynolds numbers. Overall good a posteriori performance is observed for predicting the statistics of the recirculation bubble, wall stresses and turbulence characteristics. The statistics of the modelled subgrid-scale (SGS) stresses at the first off-wall grids are compared with those calculated using the WRLES data. The comparison shows that the amplitude and distribution of the SGS stresses and energy transfer obtained using the proposed model agree better with the reference data when compared with the conventional SGS model.

Elbow, with complex physiological structure, plays an important role in upper limb motion which can be assisted with exoskeleton in rehabilitation. However, the stiffness of elbow changes while training which decline the comfort and effect of rehabilitation. Moreover, the rotation axis of elbow is changing which will cause secondary injuries. In this paper, we design an elbow exoskeleton with a variable stiffness actuator and a deviation compensation unit to assist elbow rehabilitation. Firstly, we design a variable stiffness actuator by symmetric actuation principle to adapt the change of elbow stiffness. The parameters of the variable stiffness actuator are optimized by motion simulation. Next, we design a deviation compensation unit to follow the rotation axis deviation outside the horizontal plane. The compensation area is simulated to cover the deviation. Finally, simulation and experiments are carried out to show the performance of our elbow exoskeleton. The workspace can meet the need of daily elbow motion while the variable stiffness actuator can adjust the exoskeleton stiffness as expectation.

This study demonstrates a kilowatt-level, spectrum-programmable, multi-wavelength fiber laser (MWFL) with wavelength, interval and intensity tunability. The central wavelength tuning range is 1060–1095 nm and the tunable number is controllable from 1 to 5. The wavelength interval can be tuned from 6 to 32 nm and the intensity of each channel can be adjusted independently. Maximum output power up to approximately 1100 W has been achieved by master oscillator power amplifier structures. We also investigate the wavelength evolution experimentally considering the difference of gain competition, which may give a primary reference for kW-level high-power MWFL spectral manipulation. To the best of our knowledge, this is the highest output power ever reported for a programmable MWFL. Benefiting from its high power and flexible spectral manipulability, the proposed MWFL has great potential in versatile applications such as nonlinear frequency conversion and spectroscopy.

In this work, the shape of a bluff body is optimized to mitigate velocity fluctuations of turbulent wake flows based on large-eddy simulations (LES). The Reynolds-averaged Navier–Stokes method fails to capture velocity fluctuations, while direct numerical simulations are computationally prohibitive. This necessitates using the LES method for shape optimization given its scale-resolving capability and relatively affordable computational cost. However, using LES for optimization faces challenges in sensitivity estimation as the chaotic nature of turbulent flows can lead to the blowup of the conventional adjoint-based gradient. Here, we propose using the regularized ensemble Kalman method for the LES-based optimization. The method is a statistical optimization approach that uses the sample covariance between geometric parameters and LES predictions to estimate the model gradient, circumventing the blowup issue of the adjoint method for chaotic systems. Moreover, the method allows for the imposition of smoothness constraints with one additional regularization step. The ensemble-based gradient is first evaluated for the Lorenz system, demonstrating its accuracy in the gradient calculation of the chaotic problem. Further, with the proposed method, the cylinder is optimized to be an asymmetric oval, which significantly reduces turbulent kinetic energy and meander amplitudes in the wake flows. The spectral analysis methods are used to characterize the flow field around the optimized shape, identifying large-scale flow structures responsible for the reduction in velocity fluctuations. Furthermore, it is found that the velocity difference in the shear layer is decreased with the shape change, which alleviates the Kelvin–Helmholtz instability and the wake meandering.

We demonstrated a method to improve the output performance of a Ti:sapphire laser in the long-wavelength low-gain region with an efficient stimulated Raman scattering process. By shifting the wavelength of the high-gain-band Ti:sapphire laser to the long-wavelength low-gain region, high-performance Stokes operation was achieved in the original long-wavelength low-gain region of the Ti:sapphire laser. With the fundamental wavelength tuning from 870 to 930 nm, first-order Stokes output exceeding 2.5 W was obtained at 930–1000 nm, which was significantly higher than that directly generated by the Ti:sapphire laser, accompanied by better beam quality, shorter pulse duration and narrower linewidth. Under the pump power of 42.1 W, a maximum first-order Stokes power of 3.24 W was obtained at 960 nm, with a conversion efficiency of 7.7%. Furthermore, self-mode-locked modulations of first- and second-order Stokes generation were observed in Ti:sapphire intracavity solid Raman lasers for the first time.

Increasing transboundary crises necessitate the development of crisis management capabilities that transcend boundaries. In such situations, inter-governmental and cross-functional collaboration has become a common practice to address the complexities of governance challenges. This study employs Social Network Analysis to examine the structure, function, and evolution of policy collaboration networks in China in response to COVID-19 and SARS. Since the SARS outbreak, China has embraced a collaborative governance approach, considering the transboundary nature of COVID-19. This approach has led to the involvement of numerous specialized organizations engaged in economic and social development, contributing to the establishment of a larger and more loosely connected collaboration network. While the health department bears the primary responsibility for coordinating public health emergency management, diverse organizations with social governance and economic management functions have also emerged as key actors, providing crucial anti-epidemic information, knowledge, and resources to address this significant cross-border crisis.

The formation mechanism for the stopping vortex ring (SVR) and its effects on the development of starting jets have been systematically investigated. The radial inward flow near the nozzle exit, arising from the pressure difference caused by the deceleration of starting jets, is considered to be the main contributing factor to the formation of the SVR. The formation process can generally be divided into (i) the rapid accumulation stage ($t_d^*\leq 1$) and (ii) the development stage ($t_d^*>1$), where $t_d^*$ is the formation time defined by the duration of the deceleration stage. For starting jets with different $(L/D)_d$, the final circulation value and circulation growth rate of the SVR can be scaled by $[(L/D)_d]^{-0.5}$ and $[(L/D)_d]^{-1.5}$, respectively. Here $(L/D)_d$ represents the stroke ratio during the deceleration stage. Analysing the temporal evolution of fluid parcels in the vicinity of the nozzle exit reveals that SVR entrains fluid from both inside and outside of the nozzle. Additionally, the influence of the SVR on the leading vortex ring and the trailing jet has been examined, with particular attention to its effects on the propulsive performance of the starting jet. The SVR affects the profiles of axial velocity and gauge pressure at the nozzle exit, thereby enhancing the generation of total thrust during the deceleration stage. Analysis has shown that depending on the deceleration rate, SVR can enhance the average velocity thrust by at least $10\,\%$ and compensate for up to a $60\,\%$ reduction in pressure thrust due to deceleration.

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.

In response to the complex and challenging task of long-distance inspection of small-diameter and variable-diameter mine holes, this paper presents a design for an adaptive small-sized mine hole robot. First, focusing on the environment of small-diameter mine holes, the paper analyzes the robot’s functions and overall structural framework. A two-wheeled wall-pressing robot with good mobility, arranged in a straight line, is designed. Furthermore, an adaptive variable-diameter method is devised, which involves constructing an adaptive variable-diameter model and proposing a control method based on position and force estimators, enabling the robot to perceive external forces. Lastly, to verify the feasibility of the structural design and adaptive variable-diameter method, performance tests and analyses are conducted on the robot’s mobility and adaptive variable-diameter capabilities. Experimental results demonstrate that the robot can move within small-diameter mine holes at any inclination angle, with a maximum horizontal crawling speed of 3.96 m/min. By employing the adaptive variable-diameter method, the robot can smoothly navigate convex platform obstacles and slope obstacles in mine holes with diameters ranging from 70 mm to 100 mm, achieving the function of adaptive variable-diameter within 2 s. Thus, it can meet the requirements of moving inside mine holes under complex conditions such as steep slopes and small and variable diameters.

The AIMTB rapid test assay is an emerging test, which adopted a fluorescence immunochromatographic assay to measure interferon-γ (IFN-γ) production following stimulation of effector memory T cells in whole blood by mycobacterial proteins. The aim of this article was to explore the ability of AIMTB rapid test assay in detecting Mycobacterium tuberculosis (MTB) infection compared with the widely applied QuantiFERON-TB Gold Plus (QFT-Plus) test among rural doctors in China. In total, 511 participants were included in the survey. The concordance between the QFT-Plus test and the AIMTB rapid test assay was 94.47% with a Cohen’s kappa coefficient (κ) of 0.84 (95% CI, 0.79–0.90). Improved concordance between the two tests was observed in males and in participants with 26 or more years of service as rural doctors. The quantitative values of the QFT-Plus test was higher in individuals with a result of QFT-Plus-/AIMTB+ as compared to those with a result of QFT-Plus-/AIMTB- (p < 0.001). Overall, our study found that there was an excellent consistency between the AIMTB rapid test assay and the QFT-Plus test in a Chinese population. As the AIMTB rapid test assay is fast and easy to operate, it has the potential to improve latent tuberculosis infection testing and treatment at the community level in resource-limited settings.

Previous literature generally acknowledged that leader inclusiveness has positive effects on employee voice. However, emerging research and practice commentary highlight the importance of considering the potential dark side of leader inclusiveness on employee voice. This study examines the dual-path mechanism by which leader inclusiveness influences employee voice through perceived autonomy and cognitive dependence and investigates the moderating role of performance-prove goal orientation within this dynamic. Based on data from 286 independent leader–subordinate dyads working in China, we find that leader inclusiveness can promote employee voice by increasing perceived autonomy, and hinder employee voice by increasing cognitive dependence. Furthermore, performance-prove goal orientation weakens the positive indirect effect of leader inclusiveness on voice via perceived autonomy and strengthens the negative indirect effect of leader inclusiveness on voice via cognitive dependence. These findings contribute to a better understanding of how leader inclusiveness affects employee voice behavior through dual pathways and its boundary conditions.