We investigated the dynamics of thin-layer formation by non-spherical motile phytoplankton in time-dependent shear flow, building on the seminal work of Durham et al. (2009 Science vol. 323, pp. 1067–1070), on spherical microswimmers in time-independent flows. By solving the torque balance equation for a microswimmer, we found that the system is highly damped for body sizes smaller than $10^{-3}$ m, with initial rotational motion dissipating quickly. From this torque balance, we also derived the critical shear for ellipsoidal microswimmers, which we validated numerically. Simulations revealed that the peak density of microswimmers is slightly higher than the theoretical prediction due to the speed asymmetry of sinking and gyrotaxis above and below the predicted height. In addition, we observed that microswimmers with higher aspect ratios tend to form thicker layers due to slower angular velocity. Using linear stability analysis, we identified a thin-layer accumulation time scale, which contains two regimes. This theoretically predicted accumulation time scale was validated through simulations. In time-dependent flow with oscillating critical shear depth, we identified three accumulation regimes and a transitional regime based on the ratio of swimmer and flow time scales. Our results indicate that thin layers can form across time scale ratios spanning five orders of magnitude, which helps explain the widespread occurrence of thin phytoplankton layers in natural water bodies.

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.

The underwater target detection is affected by image blurring caused by suspended particles in water bodies and light scattering effects. To tackle this issue, this paper proposes a reparameterized feature enhancement and fusion network for underwater blur object recognition (REFNet). First, this paper proposes the reparameterized feature enhancement and gathering (REG) module, which is designed to enhance the performance of the backbone network. This module integrates the concepts of reparameterization and global response normalization to enhance the network’s feature extraction capabilities, addressing the challenge of feature extraction posed by image blurriness. Next, this paper proposes the cross-channel information fusion (CIF) module to enhance the neck network. This module combines detailed information from shallow features with semantic information from deeper layers, mitigating the loss of image detail caused by blurring. Additionally, this paper replace the CIoU loss function with the Shape-IoU loss function improves target localization accuracy, addressing the difficulty in accurately locating bounding boxes in blurry images. Experimental results indicate that REFNet achieves superior performance compared to state-of-the-art methods, as evidenced by higher mAP scores on the underwater robot professional competitionand detection underwater objects datasets. REFNet surpasses YOLOv8 by approximately 1.5% in $mAP_{50:95}$ on the URPC dataset and by about 1.3% on the DUO dataset. This enhancement is achieved without significantly increasing the model’s parameters or computational load. This approach enhances the precision of target detection in challenging underwater environments.

This paper presents an innovative eight-pass laser amplifier design that effectively utilizes polarization and angular multiplexing, enjoying high gain, high extraction efficiency and compact layout. To optimize the design parameters, a general spatiotemporal model for a multi-pass amplifier is established that accounts for beam passages in different angles, and the predicted output energy and gain distribution agree well with the experimental results. The multi-pass amplifier scales the seed energy of 120 mJ to 5 J at 10 Hz and 3 J at 50 Hz, with the beam quality within three times the diffraction limit.

A clear definition of society helps prevent conceptual misunderstanding. When making practical measurement of societies, it is worth noting that social complexity is actually a jagged concept that encompasses multiple weakly correlated dimensions. Understanding such jaggedness assists interpretation of the divergence between anonymous societies and the social brain hypothesis.

Upper extremity rehabilitation robots have become crucial in stroke rehabilitation due to their high durability, repeatability, and task-specific capabilities. A significant challenge in assessing the comfort performance of these robots is accurately calculating the human-robot interaction forces. In this study, a four-degree-of-freedom (4-DOF) upper extremity rehabilitation robot mechanism, kinematically compatible with the human upper limb, is proposed. Based on this mechanism, an algorithm for estimating human-robot interaction forces is developed using Newton-Euler dynamics. A prototype of the proposed robot is constructed, and a series of comparative experiments are carried out to validate the feasibility of the proposed force estimation approach. The results indicate that the proposed method reliably predicts interaction forces with minimal deviation from experimental data, demonstrating its potential for application in upper limb rehabilitation robots. This work provides a foundation for future studies focused on comfort evaluation and optimization of rehabilitation robots, with significant practical implications for improving patient rehabilitation outcomes.

Industrial robots are widely utilized in the machining of complex parts because of their flexibility. However, their low positioning accuracy and spatial geometric error characteristics significantly limit the contour precision of robot machined parts. Therefore, in the robot machining procedure, an in situ measurement system is typically required. This study aims to enhance the trajectory accuracy of robotic machining through robotic in situ measurement and meta-heuristic optimization. In this study, a measurement-machining dual-robot system for measurement and machining is established, consisting of a measurement robot with a laser sensor mounted at the robot end and a machining robot equipped with a machining tool. In the measuring process, high-precision standard spheres are set on the edge of the machining area, and the high-precision standard geometry is measured by the measurement robot. According to measured geometry information in the local area, the trajectory accuracy for the machining robot is improved. By utilizing the standard radius of the standard spheres and adopting a meta-heuristic optimization algorithm, this study addresses the complexity of the robot kinematics model, while also overcoming local optima commonly introduced by gradient-based iterative methods. The results of the experiments in this study confirm that the proposed method markedly refines the precision of the robot machining trajectory.

This study investigates the effects of fat emulsion-based early parenteral nutrition in patients following hemihepatectomy, addressing a critical gap in clinical knowledge regarding parenteral nutrition after hemihepatectomy. We retrospectively analysed clinical data from 274 patients who received non-fat emulsion-based parenteral nutrition (non-fatty nutrition group) and 297 patients who received fat emulsion-based parenteral nutrition (fatty nutrition group) after hemihepatectomy. Fat emulsion-based early parenteral nutrition significantly reduced levels of post-operative aspartate aminotransferase, total bilirubin and direct bilirubin, while minor decreases in red blood cell and platelet counts were observed in the fatty nutrition group. Importantly, fat emulsion-based early parenteral nutrition shortened lengths of post-operative hospital stay and fasting duration, but did not affect the incidence of short-term post-operative complications. Subgroup analyses revealed that the supplement of n-3 fish oil emulsions was significantly associated with a reduced inflammatory response and risk of post-operative infections. These findings indicate that fat emulsion-based early parenteral nutrition enhances short-term post-operative recovery in patients undergoing hemihepatectomy.

In this retrospective study examining the treatment of low-risk AmpC-producing Enterobacterales bacteremia during two periods with different microbiology reporting strategies, reporting of ceftriaxone susceptibility was associated with a statistically significant decrease in carbapenem use as definitive therapy compared to when susceptibility was suppressed (21 vs 50%, p < 0.0001).

Observers were randomized to time and location across two different Neonatal Intensive Care Units (NICUs) to count hand hygiene opportunities (HHOs). Mean hourly HHO was lower at night and during use of precautions, and higher in shared rooms. HHO benchmarks can support implementation of group electronic monitoring systems in NICUs.

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. We identified inflammation-related dietary patterns (DP) and explored their associations with obesity among urbanised 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. DP were derived using reduced rank regression with forty-one food groups as predictors and high-sensitivity C-reactive protein and prognostic nutritional index as inflammatory response variables. Altitude was classified as high or ultra-high. Two DP were extracted. DP-1 was characterised by having high consumptions of sugar-sweetened beverages, savoury 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 DP 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 DP were associated with increased risks of overweight and/or obesity.

In laser systems requiring a flat-top distribution of beam intensity, beam smoothing is a critical technology for enhancing laser energy deposition onto the focal spot. The continuous phase modulator (CPM) is a key component in beam smoothing, as it introduces high-frequency continuous phase modulation across the laser beam profile. However, the presence of the CPM makes it challenging to measure and correct the wavefront aberration of the input laser beam effectively, leading to unwanted beam intensity distribution and bringing difficulty to the design of the CPM. To address this issue, we propose a deep learning enabled robust wavefront sensing (DLWS) method to achieve effective wavefront measurement and active aberration correction, thereby facilitating active beam smoothing using the CPM. The experimental results show that the average wavefront reconstruction error of the DLWS method is 0.04 μm in the root mean square, while the Shack–Hartmann wavefront sensor reconstruction error is 0.17 μm.

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.

In this article, we study the following Schrödinger equation

\begin{align*}\begin{cases}-\Delta u -\frac{\mu}{|x|^2} u+\lambda u =f(u), &\text{in}~ \mathbb{R}^N\backslash\{0\},\\\int_{\mathbb{R}^{N}}|u|^{2}\mathrm{d} x=a, & u\in H^1(\mathbb{R}^{N}),\end{cases}\end{align*}

where $N\geq 3$, a > 0, and $\mu \lt \frac{(N-2)^2}{4}$. Here $\frac{1}{|x|^2} $ represents the Hardy potential (or ‘inverse-square potential’), λ is a Lagrange multiplier, and the nonlinearity function f satisfies the general Sobolev critical growth condition. Our main goal is to demonstrate the existence of normalized ground state solutions for this equation when $0 \lt \mu \lt \frac{(N-2)^2}{4}$. We also analyse the behaviour of solutions as $\mu\to0^+$ and derive the existence of normalized ground state solutions for the limiting case where µ = 0. Finally, we investigate the existence of normalized solutions when µ < 0 and analyse the asymptotic behaviour of solutions as $\mu\to 0^-$.