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Submerged vegetation plays a subtle role in exchanging the fluid mass and energy in the vegetated flow zone, where the swaying motions of flexible plants are the important source of turbulent kinetic energy production. Flume experiments were conducted to study the modes, characteristics and factors of swaying of individual submerged flexible plants. A modified plant model in a new form, representing the highly flexible vegetation with clustered leaves, was employed. A ‘rigid-like’ synchronous swaying mode and a ‘whip-like’ asynchronous flapping mode are found to appear alternately for the individual plants. The interaction between these modes depends on the resulting local flow structure affected by the plants. Compared with a plant in isolation with the same flow Reynolds number, the swaying motions of a plant within the vegetation patch are less frequent but more prone to the synchronous mode. The eigen frequency of the motions increases linearly with an increase in flow Reynolds number in the range of 2 × 104–5 × 104, but the normalised amplitude reaches a saturation at a high flow Reynolds number. Moreover, the in-line and spanwise motions have a 2 : 1 frequency ratio for an ‘8’ shaped trajectory on the horizontal plane and a 1 : 1 ratio for a ‘0’ shaped circular trajectory, or a combination of both.
It is very challenging for robots to perform grinding and polishing tasks on surfaces with unknown geometry. Most existing methods solve this problem by modeling the relationship between the force sensing information and surface normal vectors by analyzing the forces on special end tools such as spherical tools and cylindrical tools and simplified friction model. In this paper, we propose a normal vectors learning method to simultaneously control end-effector force and direction on unknown surfaces. First, the relation that mapping the force sensing information to the surface normal vectors is learned from the demonstrated data on the known plane using locally weighted regression. Next, the learned relation is used to estimate surface normal vectors on the unknown surface. To improve the force control precision on the unknown geometry surface, the adaptive force control is developed. To improve the direction control precision due to friction, the iterative learning control is developed. The proposed method is verified by comparative simulations and experiments using the Franka robot. Results show that the end-effector can be controlled perpendicular to the surface with a certain force.
In this paper, we study the local receptivity of the inviscid Mack modes in hypersonic boundary layers induced by the interaction between a surface heating or cooling source (HCS) and a freestream acoustic wave. The asymptotic analysis reveals that among the three distinguished layers, i.e. the main, wall and Stokes layers, the leading-order receptivity is attributed to the interaction of the HCS-induced mean-flow distortion and the acoustic signature in the wall layer; the second-order contribution appears in the Stokes layer; the third-order contribution appears in both the main and wall layers. Interestingly, at a moderate Reynolds number, the third-order contribution to the receptivity efficiency may be quantitatively greater than the second-order one, but this does not lead to breakdown of this asymptotic theory. Assuming the HCS intensity to be sufficiently weak, the asymptotic predictions are made for four representative cases involving different Mach numbers and wall temperatures, which are compared with the results obtained by the finite-Reynolds-number theory based on either the extended compressible Orr–Sommerfeld equations or the harmonic linearised Navier–Stokes (HLNS) calculations. Taking into account the first three orders of the receptivity efficiency, the asymptotic predictions are confirmed to be sufficiently accurate even when the Reynolds number is a few thousands, and the agreement with the finite-Reynolds-number calculations is better when the wall temperature of the base flow approaches the adiabatic wall temperature. The HLNS calculations are also conducted for moderate HCS intensities. It is found that the nonlinearity does not affect the receptivity coefficient much even when the temperature distortion of the HCS reaches $80\,\%$ of the temperature at the wall.
We test the signaling view of corporate social responsibility (CSR) engagement using two complementary quasi-natural experiments that impose exogenous negative pressure on stock prices. Firms under such adverse price pressure increase CSR activities compared to otherwise similar firms. This effect concentrates among firms with stronger signaling incentives, namely, those facing greater information asymmetry, more product market competition, higher shareholder litigation risk, and higher stock price crash risk. Firms under the exogenous negative price pressure mainly improve CSR strengths, including costly environmental investments. We also find that CSR engagement attracts socially responsible investors and lowers the cost of capital for signaling firms.
A new approach to target development for laboratory astrophysics experiments at high-power laser facilities is presented. With the dawn of high-power lasers, laboratory astrophysics has emerged as a field, bringing insight into physical processes in astrophysical objects, such as the formation of stars. An important factor for success in these experiments is targetry. To date, targets have mainly relied on expensive and challenging microfabrication methods. The design presented incorporates replaceable machined parts that assemble into a structure that defines the experimental geometry. This can make targets cheaper and faster to manufacture, while maintaining robustness and reproducibility. The platform is intended for experiments on plasma flows, but it is flexible and may be adapted to the constraints of other experimental setups. Examples of targets used in experimental campaigns are shown, including a design for insertion in a high magnetic field coil. Experimental results are included, demonstrating the performance of the targets.
Intertemporal choices involve tradeoffs between outcomes that occur at different times. Most of the research has used pure gains tasks and the discount rates yielding from those tasks to explain and predict real-world behaviors and consequences. However, real decisions are often more complex and involve mixed outcomes (e.g., sooner-gain and later-loss or sooner-loss and later-gain). No study has used mixed gain-loss intertemporal tradeoff tasks to explain and predict real-world behaviors and consequences, and studies involving such tasks are also scarce. Considering that tasks involving a combination of gains and losses may yield different discount rates and that existing pure gains tasks do not explain or predict real-world outcomes well, this study conducted two experiments to compare the discount rates of mixed gain-loss intertemporal tradeoffs with those of pure gains or pure losses (Experiment 1) and to examine whether these tasks predicted different real-world behaviors and consequences (Experiment 2). Experiment 1 suggests that the discount rate ordering of the four tasks was, from highest to lowest, pure gains, sooner-loss and later-gain, pure losses, and sooner-gain and later-loss. Experiment 2 indicates that the evidence supporting the claim that the discount rates of the four tasks were related to different real-world behaviors and consequences was insufficient.
Despite increasing knowledge on the neuroimaging patterns of eating disorder (ED) symptoms in non-clinical populations, studies using whole-brain machine learning to identify connectome-based neuromarkers of ED symptomatology are absent. This study examined the association of connectivity within and between large-scale functional networks with specific symptomatic behaviors and cognitions using connectome-based predictive modeling (CPM).
CPM with ten-fold cross-validation was carried out to probe functional networks that were predictive of ED-associated symptomatology, including body image concerns, binge eating, and compensatory behaviors, within the discovery sample of 660 participants. The predictive ability of the identified networks was validated using an independent sample of 821 participants.
The connectivity predictive of body image concerns was identified within and between networks implicated in cognitive control (frontoparietal and medial frontal), reward sensitivity (subcortical), and visual perception (visual). Crucially, the set of connections in the positive network related to body image concerns identified in one sample was generalized to predict body image concerns in an independent sample, suggesting the replicability of this effect.
These findings point to the feasibility of using the functional connectome to predict ED symptomatology in the general population and provide the first evidence that functional interplay among distributed networks predicts body shape/weight concerns.
In this work, we propose using an ensemble Kalman method to learn a nonlinear eddy viscosity model, represented as a tensor basis neural network, from velocity data. Data-driven turbulence models have emerged as a promising alternative to traditional models for providing closure mapping from the mean velocities to Reynolds stresses. Most data-driven models in this category need full-field Reynolds stress data for training, which not only places stringent demand on the data generation but also makes the trained model ill-conditioned and lacks robustness. This difficulty can be alleviated by incorporating the Reynolds-averaged Navier–Stokes (RANS) solver in the training process. However, this would necessitate developing adjoint solvers of the RANS model, which requires extra effort in code development and maintenance. Given this difficulty, we present an ensemble Kalman method with an adaptive step size to train a neural-network-based turbulence model by using indirect observation data. To our knowledge, this is the first such attempt in turbulence modelling. The ensemble method is first verified on the flow in a square duct, where it correctly learns the underlying turbulence models from velocity data. Then the generalizability of the learned model is evaluated on a family of separated flows over periodic hills. It is demonstrated that the turbulence model learned in one flow can predict flows in similar configurations with varying slopes.
Due to the lack of research between the inner layers in the structure of colonic mucous and the metabolism of fatty acid in the constipation model, we aim to determine the changes in the mucous phenotype of the colonic glycocalyx and the microbial community structure following treatment with Rhubarb extract in our research. The constipation and treatment models are generated using adult male C57BL/6N mice. We perform light microscopy and transmission electron microscopy (TEM) to detect a Muc2-rich inner mucus layer attached to mice colon under different conditions. In addition, 16S rDNA sequencing is performed to examine the intestinal flora. According to TEM images, we demonstrate that Rhubarb can promote mucin secretion and find direct evidence of dendritic structure-linked mucus structures with its assembly into a lamellar network in a pore size distribution in the isolated colon section. Moreover, the diversity of intestinal flora has noticeable changes in constipated mice. The present study characterizes a dendritic structure and persistent cross-links have significant changes accompanied by the alteration of intestinal flora in feces in models of constipation and pretreatment with Rhubarb extract.
There is increasing attention on the association of socioeconomic status and individual behaviors (SES/IB) with mental health. However, the impacts of SES/IB on mental disorders are still unclear. To provide evidence for establishing feasible strategies on disease screening and prevention, we implemented Mendelian randomization (MR) design to appraise causality between SES/IB and mental disorders.
We conducted a two-sample MR study to assess the causal effects of SES and IB (dietary habits, habitual physical activity, smoking behaviors, drinking behaviors, sleeping behaviors, leisure sedentary behaviors, risky behaviors, and reproductive behaviors) on three mental disorders, including bipolar disorder, major depressive disorder and schizophrenia. A series of filtering steps were taken to select eligible genetic instruments robustly associated with each of the traits. Inverse variance weighted was used for primary analysis, with alternative MR methods including MR-Egger, weighted median, and weighted mode estimate. Complementary methods were further used to detect pleiotropic bias.
After Bonferroni correction and rigorous quality control, we identified that SES (educational attainment), smoking behaviors (smoking initiation, number of cigarettes per day), risky behaviors (adventurousness, number of sexual partners, automobile speeding propensity) and reproductive behavior (age at first birth) were causally associated with at least one of the mental disorders.
MR study provides robust evidence that SES/IB play broad impacts on mental disorders.
Diarrhoea caused by pathogens such as enterotoxigenic E. coli (ETEC) is a serious threat to the health of young animals and human infants. Here, we investigated the protective effect of fructo-oligosaccharides (FOS) on the intestinal epithelium with ETEC challenge in a weaned piglet model. Twenty-four weaned piglets were randomly divided into three groups: (1) non-ETEC-challenged control (CON); (2) ETEC-challenged control (ECON); and (3) ETEC challenge + 2·5 g/kg FOS (EFOS). On day 19, the CON pigs were orally infused with sterile culture, while the ECON and EFOS pigs were orally infused with active ETEC (2·5 × 109 colony-forming units). On day 21, pigs were slaughtered to collect venous blood and small intestine. Result showed that the pre-treatment of FOS improved the antioxidant capacity and the integrity of intestinal barrier in the ETEC-challenged pigs without affecting their growth performance. Specifically, compared with ECON pigs, the level of GSH peroxidase and catalase in the plasma and intestinal mucosa of EFOS pigs was increased (P < 0·05), and the intestinal barrier marked by zonula occluden-1 and plasmatic diamine oxidase was also improved in EFOS pigs. A lower level (P < 0·05) of inflammatory cytokines in the intestinal mucosa of EFOS pigs might be involved in the inhibition of TLR4/MYD88/NF-κB pathway. The apoptosis of jejunal cells in EFOS pigs was also lower than that in ECON pigs (P < 0·05). Our findings provide convincing evidence of possible prebiotic and protective effect of FOS on the maintenance of intestinal epithelial function under the attack of pathogens.
The ship safety domain plays a significant role in collision risk assessment. However, few studies take the practical considerations of implementing this method in the vicinity of bridge-waters into account. Therefore, historical automatic identification system data is utilised to construct and analyse ship domains considering ship–ship and ship–bridge collisions. A method for determining the closest boundary is proposed, and the boundary of the ship domain is fitted by the least squares method. The ship domains near bridge-waters are constructed as ellipse models, the characteristics of which are discussed. Novel fuzzy quaternion ship domain models are established respectively for inland ships and bridge piers, which would assist in the construction of a risk quantification model and the calculation of a grid ship collision index. A case study is carried out on the multi-bridge waterway of the Yangtze River in Wuhan, China. The results show that the size of the ship domain is highly correlated with the ship's speed and length, and analysis of collision risk can reflect the real situation near bridge-waters, which is helpful to demonstrate the application of the ship domain in quantifying the collision risk and to characterise the collision risk distribution near bridge-waters.
Frozen embryo transfer (FET) has been adopted by growing number of reproductive medicine centers due to the improved outcome compared with fresh embryo transfer. However, few studies have focused on the impact of embryo cryopreservation duration on pregnancy-related complications and neonatal birthweight. Thus, a retrospective cohort study including all FET cycles with livebirth deliveries in a university affiliated hospital from May 2010 to September 2017 was conducted. These deliveries were grouped by the cryopreservation duration of the transferred embryo (≤3 months, 4–6 months, 7–12 months, and >12 months). The associations between embryo cryopreservation duration and pregnancy-related complications were evaluated among the groups using multinomial logistic regression. Neonatal birthweight was compared according to the stratification of singletons and multiples using multinomial and multilevel logistic regression, respectively. Among all 12,158 FET cycles, a total of 3864 livebirth deliveries comprising 2995 singletons and 1739 multiples were included. Compared with those within 3 months, women undergoing FET after a cryopreservation time longer than 3 months did not show any increased risk of gestational diabetes mellitus, gestational hypertension, preeclampsia, meconium staining of the amniotic fluid, or preterm birth. Furthermore, the risk of lower birthweight, macrosomia, small-for-gestational-age, or large-for-gestational-age for either singletons or multiples was not affected by long-term cryopreservation. In summary, embryo cryopreservation duration does not have negative effects on pregnancy-related complications or birthweight after FET.
This chapter introduces the fundamental elements of random matrix theory and highlights key applications in line outage detection using actual data recovered from existing power systems around the globe. The key mathematical component is a novel concept referred to as the mean spectral radius (MSR) of non-Hermitian random matrices. By analyzing the changes of the MSR of random matrices, grid failure detection is reliably achieved. Several studies and simulations are considered to observe the performance of this new theoretical approach to line outage detection.
To detect low concentrations of formaldehyde selectively, the sensing properties of SnO2 nanostructured are enhanced by modifying with p-type semiconductor NiO. In this study, a nanostructured SnO2/NiO composite was prepared by a simple hydrothermal method. The X-ray photoelectron spectroscopy (XPS) peak in 532.4 eV proved that the existence of the SnO2/NiO composite structure increased the amount of adsorbed oxygen O− and O2− significantly. Gas-sensing tests showed that these mixed phases SnO2/NiO are highly promising for gas sensor applications, as the gas response for formaldehyde was significantly enhanced in gas response, selectivity at an operating temperature of 230 °C. The sensor fabricated by SnO2/NiO composite can detect as low as 1 ppm of formaldehyde at 230 °C, and the corresponding response is 1.57. The results of physicochemical properties tests of the samples show that the enhancement in sensitivity and selectivity is attributed to the oxygen vacancies and heterojunction between SnO2 and NiO. The SnO2/NiO composites can be applied to sensitive materials of formaldehyde sensors.
During pulsar navigation, the high-frequency noise carried by the pulsar profile signal reduces the accuracy of the pulse TOA (Time of Arrival) estimation. At present, the main method to remove signal noise by using wavelet transform is to redesign the function of the threshold and level of wavelet transform. However, the signal-to-noise ratio and other indicators of the filtered signal need to be further optimised, so a more appropriate wavelet basis needs to be designed. This paper proposes a wavelet basis design method based on frequency domain analysis to improve the denoising effect of pulsar signals. This method first analyses the pulsar contour signal in the frequency domain and then designs a Crab pulsar wavelet basis (CPn, where n represents the wavelet basis length) based on its frequency domain characteristics. In order to improve the real-time performance of the algorithm, a wavelet lifting scheme is implemented. Through simulation, this method analyses the pulsar contour signal data at home and abroad. Results show the signal-to-noise ratio can be increased by 4 dB, the mean square error is reduced by 61% and the peak error is reduced by 45%. Therefore, this method has better filtering effect.
The aim of this study was to analyze the profile of chest injuries, oxygen therapy for respiratory failure, and the outcomes of victims after the Jiangsu tornado, which occurred on June 23, 2016 in Yancheng City, Jiangsu Province, China.
The clinical records of 144 patients referred to Yancheng City No.1 People’s Hospital from June 23 through June 25 were retrospectively investigated. Of those patients, 68 (47.2%) sustained major chest injuries. The demographic details, trauma history, details of injuries and Abbreviated Injury Scores (AIS), therapy for respiratory failure, surgical procedures, length of intensive care unit (ICU) and hospital stay, and mortality were analyzed.
Of the 68 patients, 41 (60.3%) were female and 27 (39.7%) were male. The average age of the injured patients was 57.1 years. Forty-six patients (67.6%) suffered from polytrauma. The mean thoracic AIS of the victims was calculated as 2.85 (SD = 0.76). Rib fracture was the most common chest injury, noted in 56 patients (82.4%). Pulmonary contusion was the next most frequent injury, occurring in 12 patients (17.7%). Ten patients with severe chest trauma were admitted to ICU. The median ICU stay was 11.7 (SD = 8.5) days. Five patients required intubation and ventilation, one patient was treated with noninvasive positive pressure ventilation (NPPV), and four patients were treated with high-flow nasal cannula (HFNC). Three patients died during hospitalization. The hospital mortality was 4.41%.
Chest trauma was a common type of injury after tornado. The most frequent thoracic injuries were rib fractures and pulmonary contusion. Severe chest trauma is usually associated with a high incidence of respiratory support requirements and a long length of stay in the ICU. Early initiation of appropriate oxygen therapy was vital to restoring normal respiratory function and saving lives. Going forward, HFNC might be an effective and well-tolerated therapeutic addition to the management of acute respiratory failure in chest trauma.