Thanks to its real-time computation efficiency, deep reinforcement learning (DRL) has been widely applied in motion planning for mobile robots. In DRL-based methods, a DRL model computes an action for a robot based on the states of its surrounding obstacles, including other robots that may communicate with it. These methods always assume that the environment is attack-free and the obtained obstacles’ states are reliable. However, in the real world, a robot may suffer from obstacle localization attacks (OLAs), such as sensor attacks, communication attacks, and remote-control attacks, which cause the robot to retrieve inaccurate positions of the surrounding obstacles. In this paper, we propose a robust motion planning method ObsGAN-DRL, integrating a generative adversarial network (GAN) into DRL models to mitigate OLAs in the environment. First, ObsGAN-DRL learns a generator based on the GAN model to compute the approximation of obstacles’ accurate positions in benign and attack scenarios. Therefore, no detectors are required for ObsGAN-DRL. Second, by using the approximation positions of the surrounding obstacles, ObsGAN-DRL can leverage the state-of-the-art DRL methods to compute collision-free motion commands (e.g., velocity) efficiently. Comprehensive experiments show that ObsGAN-DRL can mitigate OLAs effectively and guarantee safety. We also demonstrate the generalization of ObsGAN-DRL.

Previous studies have revealed an association between dietary factors and atopic dermatitis (AD). To explore whether there was a causal relationship between diet and AD, we performed Mendelian randomisation (MR) analysis. The dataset of twenty-one dietary factors was obtained from UK Biobank. The dataset for AD was obtained from the publicly available FinnGen consortium. The main research method was the inverse-variance weighting method, which was supplemented by MR‒Egger, weighted median and weighted mode. In addition, sensitivity analysis was performed to ensure the accuracy of the results. The study revealed that beef intake (OR = 0·351; 95 % CI 0·145, 0·847; P = 0·020) and white bread intake (OR = 0·141; 95 % CI 0·030, 0·656; P = 0·012) may be protective factors against AD. There were no causal relationships between AD and any other dietary intake factors. Sensitivity analysis showed that our results were reliable, and no heterogeneity or pleiotropy was found. Therefore, we believe that beef intake may be associated with a reduced risk of AD. Although white bread was significant in the IVW analysis, there was large uncertainty in the results given the wide 95 % CI. Other factors were not associated with AD in this study.

Very-large-scale motions are commonly observed in moderate- and high-Reynolds-number wall turbulence, constituting a considerable portion of the Reynolds stress and skin friction. This study aims to investigate the behaviour of these motions in high-speed and high-Reynolds-number turbulent boundary layers at varying Mach numbers. With the aid of high-precision numerical simulations, numerical experiments and theoretical analysis, it is demonstrated that the very-large-scale motions are weakened in high-Mach-number turbulence at the same friction Reynolds numbers, leading to the reduction in turbulent kinetic energy in the outer region. Conversely, the lower wall temperature enhances the very-large-scale motions but shortens the scale separation between the structures in the near-wall and outer regions.

Taste preference is a pivotal predictor of nutrient intake, yet its impact on mild cognitive impairment (MCI) remains poorly understood. We aimed to investigate the association between taste preferences and MCI and the role of cardiometabolic diseases (CMD) in this association. The study included older adults, aged 65–90 years, with normal cognitive function at baseline who were enrolled in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) from 2008 to 2018. MCI was measured by the Mini-Mental State Examination, and multivariable Cox regression models were applied. Among 6423 participants, 2534 (39·45 %) developed MCI with an incidence rate of 63·12 - per 1000 person-years. Compared with individuals with insipid taste, those preferring sweetness or spiciness had a higher MCI risk, while saltiness was associated with a lower risk. This association was independent of objective dietary patterns and was more pronounced among urban residents preferring sweetness and illiterate participants preferring spiciness. Notably, among sweet-liking individuals, those with one CMD experienced a significant detrimental effect, and those with co-occurring CMD had a higher incidence rate of MCI. Additionally, regional variations were observed: sweetness played a significant role in regions known for sweet cuisine, while the significance of spiciness as a risk factor diminishes in regions where it is commonly preferred. Our findings emphasize the role of subjective taste preferences in protecting cognitive function and highlight regional variations. Target strategies should focus on assisting individuals with CMD to reduce excessive sweetness intake and simultaneously receiving treatment for CMD to safeguard cognitive function.

Risk of bias assessment is a critical step of any meta-analysis or systematic review. Given the low sample count of many microbiome studies, especially observational or cohort studies involving human subjects, many microbiome studies have low power. This increases the importance of performing meta-analysis and systematic review for microbiome research in order to enhance the relevance and applicability of microbiome results. This work proposes a method based on the ROBINS-I tool to systematically consider sources of bias in microbiome research seeking to perform meta-analysis or systematic review for microbiome studies.

Following the stability analysis method in classic fluid dynamics, a linear stability equation (LSE) suitable for rarefied flows is derived based on the Bhatnagar–Gross–Krook (BGK) equation. The global method and singular value decomposition method are used for modal and non-modal analysis, respectively. This approach is validated by results obtained from Navier–Stokes (NS) equations. The modal analysis shows that LSEs based on NS equations (NS-LSEs) begin to fail when the Knudsen number ($Kn$) increases past $\sim$0.01, regardless of whether a slip model is used. When $Kn\geq 0.01$, the growth rate of the least stable mode is generally underestimated by the NS-LSEs. Under a fixed wavenumber, the pattern (travelling or standing wave) of the least stable mode changes with $Kn$; when the mode presents the same pattern, the growth rate decreases almost linearly with increasing $Kn$; otherwise, rarefaction effects may not stabilize the flow. The characteristic lengths of the different modes are different, and the single-scale classic stability analysis method cannot predict multiple modes accurately, even when combined with a slip model and even for continuum flow. However, non-modal analysis shows that this error does not affect the transient growth because modes with small growth rates offer little contribution to the transient growth. In rarefied flow, as long as the Mach number ($Ma$) is large enough, transient growth will occur in some wavenumber ranges. The rarefaction effect plays a stabilizing role in transient growth. The NS-LSEs-based method always overestimates the maximum transient growth.

The oblique shock impinging on the supersonic turbulent boundary layer leads to a mixing layer and the emergence of large-scale coherent structures within the interaction zone which leave significant velocity defect and turbulence amplification downstream. In the present study, we investigate the turbulence recovery in the post-shock region by exploiting direct numerical simulation data of the oblique-shock/turbulent boundary layer interaction flow at the incoming Mach number of $2.28$ and the shock angle of $33.2^\circ$, with special attention paid to the contribution of the mixing layer and large-scale structures to flow dynamics. For that purpose, we propose to split the mean velocity, Reynolds stresses and spanwise spectra into a canonical portion that is constructed according to the statistics of canonical turbulent boundary layers, and a mixing-layer-induced portion. We found that the hidden mixing layer grows with the boundary layer thickness and that the induced mean shear and Reynolds stresses decay at different rates. The mean velocity recovers to the canonical profiles at a distance of 13 boundary layer thicknesses downstream where the mixing-layer-induced mean shear ceases to have strong impacts. The recovery of Reynolds stresses requires 10 boundary layer thicknesses in the near-wall region but a much longer streamwise extent in the outer region due to the slow decay of large-scale motions. These large-scale motions superpose on the near-wall turbulence, intensifying the turbulent fluctuations, yet having a trivial impact on the skin friction, for the contribution of the mixing-layer-induced mean shear and Reynolds shear stress are balanced by the advection term. We further establish a simple physical model capable of approximately predicting the streamwise evolution of mixing-layer-induced mean shear and turbulent kinetic energy. This model suggests that the complete recovery of turbulence in the outer region requires a streamwise extent of approximately 50 boundary layer thicknesses.

Nitrogen fertilizer and water are two major nutrients required for the optimal production of rice worldwide. The utilization of different irrigation techniques to save water and fertigation to maximize rice production has been the main focus. A field experiment was conducted to explore the responses of 16 rice varieties to different irrigation and nitrogen fertilizer regimes. Two nitrogen treatments, 270 kg ha−1 and 225 kg ha−1 (urea N ≥ 46.4%), and two irrigation regimes, 8.7 t ha−1 and 5.22 t ha−1, were applied three times. Plant height and the soil and plant analyser development (SPAD) values were measured throughout the growth period. The total yield and quality characteristics of the rice varieties were also determined. Based on the yield, the 16 rice varieties were divided into three groups: high yield (I), middle yield (II) and low yield (III) using cluster analysis. A positive correlation was found between the growth period and yield of these 16 rice varieties. In the water-deficient regime, the growth period of the 16 varieties was reduced by 1.68–2.93%. Furthermore, nitrogen- and water-deficient regimes had significant effects on the polishing rate, protein content and taste values of all varieties. At maturity stage under these regimes, plant height and chlorophyll SPAD values were decreased by 1.25–6.05% and 1.60–31.48%, respectively. Deficient nitrogen fertilization, along with appropriate irrigation, is an effective method for the efficient utilization of irrigation and fertilizer resources in rice-growing areas.

Turbulence amplification and the large-scale coherent structures in shock wave/turbulent boundary layer interaction flows have been studied at length in previous research, while the direct association between these two flow features is still lacking. In the present study, the transport equation of turbulent kinetic energy spectra is derived and utilized to analyse the scale-by-scale energy budget across the interaction zone, enabling us to reveal the association between the genesis of the large-scale motions and the turbulence amplification. For the presently considered flow with incipient shock-induced separation, we identified in turbulent kinetic energy spectra distribution that the most energetic motions are converted from the near-wall small-scale motions to large-scale motions consisting of velocity streaks and cross-stream circulations as they go through the interaction zone. The amplification of streamwise velocity fluctuation is triggered first, resulting in the emergence of large-scale velocity streaks, which is attributed to the adverse pressure gradient, as indicated by the spectra of the production term. The energy carried by large-scale velocity streaks is transferred to other velocity components by the pressure-strain term, producing large-scale cross-stream circulations. When large-scale motions are convected downstream, their energy is transferred via turbulent cascade to smaller scales and dissipated by viscosity. The spanwise uniform fluctuations, reminiscent of the unsteadiness of the separation bubble, are contributed primarily by the inter-scale energy transfer from the finite spanwise scale motions.

Collision avoidance is critical in multirobot systems. Most of the current methods for collision avoidance either require high computation costs (e.g., velocity obstacles and mathematical optimization) or cannot always provide safety guarantees (e.g., learning-based methods). Moreover, they cannot deal with uncertain sensing data and linguistic requirements (e.g., the speed of a robot should not be large when it is near to other robots). Hence, to guarantee real-time collision avoidance and deal with linguistic requirements, a distributed and hybrid motion planning method, named Fuzzy-VO, is proposed for multirobot systems. It contains two basic components: fuzzy rules, which can deal with linguistic requirements and compute motion efficiently, and velocity obstacles (VOs), which can generate collision-free motion effectively. The Fuzzy-VO applies an intruder selection method to mitigate the exponential increase of the number of fuzzy rules. In detail, at any time instant, a robot checks the robots that it may collide with and retrieves the most dangerous robot in each sector based on the predicted collision time; then, the robot generates its velocity in real-time via fuzzy inference and VO-based fine-tuning. At each time instant, a robot only needs to retrieve its neighbors’ current positions and velocities, so the method is fully distributed. Extensive simulations with a different number of robots are carried out to compare the performance of Fuzzy-VO with the conventional fuzzy rule method and the VO-based method from different aspects. The results show that: Compared with the conventional fuzzy rule method, the average success rate of the proposed method can be increased by 306.5%; compared with the VO-based method, the average one-step decision time is reduced by 740.9%.

Excitation–emission-spectral unmixing-based fluorescence resonance energy transfer (ExEm-spFRET) microscopy exhibits excellent robustness in living cells. We here develop an automatic ExEm-spFRET microscope with 3.04 s of time resolution for a quantitative FRET imaging. The user-friendly interface software has been designed to operate in two modes: administrator and user. Automatic background recognition, subtraction, and cell segmentation were integrated into the software, which enables FRET calibration or measurement in a one-click operation manner. In administrator mode, both correction factors and spectral fingerprints are only calibrated periodically for a stable system. In user mode, quantitative ExEm-spFRET imaging is directly implemented for FRET samples. We implemented quantitative ExEm-spFRET imaging for living cells expressing different tandem constructs (C80Y, C40Y, C10Y, and C4Y, respectively) and obtained consistent results for at least 3 months, demonstrating the stability of our microscope. Next, we investigated Bcl-xL-Bad interaction by using ExEm-spFRET imaging and FRET two-hybrid assay and found that the Bcl-xL-Bad complexes exist mainly in Bad-Bcl-xL trimers in healthy cells and Bad-Bcl-xL2 trimers in apoptotic cells. We also performed time-lapse FRET imaging on our system for living cells expressing Yellow Cameleon 3.6 (YC3.6) to monitor ionomycin-induced rapid extracellular Ca2+ influx with a time interval of 5 s for total 250 s.

This paper develops the conceptual design and error analysis of a cable-driven parallel robot (CDPR). The earlier error analysis of CDPRs generally regarded the cable around the pulley as a center point and neglected the radius of the pulleys. In this paper, the conceptual design of a CDPR with pulleys on its base platform is performed, and an error mapping model considering the influence of radius of the pulleys for the CDPR is established through kinematics analysis and a full matrix complete differential method. Monte Carlo simulation is adopted to deal with the sensitivity analysis, which can directly describe the contribution of each error component to the total orientation error of the CDPR by virtue of the error modeling. The results show that the sensitivity coefficients of pulleys’ geometric errors and geometric errors of the cables are relatively larger, which confirms that the cable length errors and pulleys’ geometric errors should be given higher priority in design and processing.

Accurate predetermination of the quantum yield ratio (QA/QD) and the extinction coefficient ratio (KA/KD) between acceptor and donor is a prerequisite for quantitative fluorescence resonance energy transfer (FRET) imaging. We here propose a method to measure KA/KD and QA/QD by measuring the excitation–emission spectra (ExEm-spectra) of one dish of cells expressing m (≥3) kinds of FRET constructs. The ExEm-spectra images are unmixed to obtain the weight maps of donor (WD), acceptor (WA), and acceptor sensitization (WS). For each cell, the frequency distribution plots of the WS/WD and WS/WA images are fitted by using a single-Gaussian function to obtain the peak values of WS/WD (SD) and WS/WA (SA). The statistical frequency-SD/SA plots from all cells are fitted by using a multi-Gaussian function to obtain the peak values of both SD and SA, and then the ranges of WS/WD (RSD) and WS/WA (RSA) for each FRET construct are predetermined. Based on the predetermined RSD and RSA values of FRET constructs, our method is capable of automatically classifying cells expressing different FRET constructs. Finally, the WS/WD–WA/WD plot from different kinds of cells is linearly fitted to obtain KA/KD and QA/QD values.