432 research outputs found
Time-dependent behaviour of composite steel-concrete slabs prepared with recycled coarse aggregate
The purpose of this research is to investigate the influence of recycled coarse aggregate (RCA) on the long-term behaviour of composite steel-concrete slabs. Several types of tests were conducted to fulfil the research, including material tests on recycled aggregate concrete (RAC), non-uniform shrinkage tests on small-scale slabs, and long-term tests on full-scale slabs. In the materials tests, the influence of service time of RCA and mixing methods on the mechanical properties of RAC were first investigated. The model to predict the RAC elastic modulus was developed accounting for the influence of residual mortar content. The shrinkage tests were then conducted on RAC using RCA obtained from parent concrete having different service time and compressive strengths. The models to predict the autogenous and drying shrinkage behaviour of RAC were developed accounting for the combined effects of parent concrete quality and residual mortar content. In the shrinkage tests on the small-scale slabs, the relative humidity (RH) and strain distributions through the thickness of seventeen slabs were monitored over time. Test parameters consisted of RCA replacement ratios, slab depths and sealing conditions of the slab surfaces. The mechanism of non-uniform shrinkage in composite slabs was revealed by the measured RH distributions through the thickness of slabs. In the long-term tests on the full-scale slabs, seven slabs were prepared and measured, including three steel-bars truss slabs and four composite slabs. A nonlinear finite element model was developed to account for the time-dependent behaviour of composite slabs to include the effects of non-uniform shrinkage, creep and concrete cracking. Based on the experimental investigations and numerical simulations, a design approach to be used for routine design of RAC composite slabs was proposed and validated
Detection of the deep-sea plankton community in marine ecosystem with underwater robotic platform.
Variations in the quantity of plankton impact the entire marine ecosystem. It is of great significance to accurately assess the dynamic evolution of the plankton for monitoring the marine environment and global climate change. In this paper, a novel method is introduced for deep-sea plankton community detection in marine ecosystem using an underwater robotic platform. The videos were sampled at a distance of 1.5 m from the ocean floor, with a focal length of 1.5–2.5 m. The optical flow field is used to detect plankton community. We showed that for each of the moving plankton that do not overlap in space in two consecutive video frames, the time gradient of the spatial position of the plankton are opposite to each other in two consecutive optical flow fields. Further, the lateral and vertical gradients have the same value and orientation in two consecutive optical flow fields. Accordingly, moving plankton can be accurately detected under the complex dynamic background in the deep-sea environment. Experimental comparison with manual ground-truth fully validated the efficacy of the proposed methodology, which outperforms six state-of-the-art approaches
Effect of Liuweibuqi capsule, a Chinese patent medicine, on the JAK1/STAT3 pathway and MMP9/TIMP1 in a chronic obstructive pulmonary disease rat model
AbstractObjectiveTo observe effect of Liuweibuqi Capsule, a Traditional Chinese Medicine (TCM), on the janus kinase (JAK)/signal transducer and activator of transcription (STAT) pathway and matrix metalloproteinases (MMPs) in a chronic obstructive pulmonary disease (COPD) rat model with lung deficiency in terms of TCM's pattern differentiation.MethodsRats were randomly divided into a normal group, model group, Liuweibuqi group, Jinshuibao group, and spleen aminopeptidase group (n= 10). Aside from the normal group, all rats were exposed to smoke plus lipopolysaccharide tracheal instillation to establish the COPD model with lung deficiency. Models were established after 28 days and then the normal and model groups were given normal saline (0.09 g/kg), Liuweibuqi group was given Liuweibuqi capsule (0.35 g/kg), Jinshuibao group was given Jinshuibao capsules (0.495 g/kg), and the spleen group was given spleen aminopeptidase (0.33 mg/kg), once a day for 30 days. Changes in symptoms, signs, and lung histology were observed. Lung function was measured with a spirometer. Serum cytokines were detected using enzyme-linked immunosorbent assay, and changes in the JAK/STAT pathway, MMP-9, and MMPs inhibitor 1 (TIMP1) were detected by immunohistochemistry, RT-PCR, and western blotting, respectively.ResultsCompared with the normal group, lung tissue was damaged, and lung function was reduced in the model control group. Additionally, the levels of interleukin (IL)-1β, γ interferon (IFN-γ), and IL-6 were higher, while IL-4 and IL-10 were lower in the model control group than those in the normal group. The expressions of JAK1, STAT3, p-STAT3, and MMP-9 mRNA and protein in lung tissue were higher, and TIMP1 mRNA and protein was lower in the model group compared with the normal group. After treatment, compared with the model group, the expression of inflammatory cytokines was lower in each treatment group, and expressions of JAK/STAT pathway, MMPs were lower. Compared with the positive control groups, the Jinshuibao and spleen aminopeptidase groups, lung function was better, and JAK1, STAT3, and p-STAT3 protein were lower and TIMP1 was higher in the Liuweibuqi group.ConclusionLiuweibuqi capsules can improve the symptoms of COPD possibly by regulating the expression of the JAK1/STAT3 pathway and MMP9/TIMP1
Dynamic RACH Partition for Massive Access of Differentiated M2M Services
In machine-to-machine (M2M) networks, a key challenge is to overcome the overload problem caused by random access requests from massive machine-type communication (MTC) devices. When differentiated services coexist, such as delay-sensitive and delay-tolerant services, the problem becomes more complicated and challenging. This is because delay-sensitive services often use more aggressive policies, and thus, delay-tolerant services get much fewer chances to access the network. To conquer the problem, we propose an efficient mechanism for massive access control over differentiated M2M services, including delay-sensitive and delay-tolerant services. Specifically, based on the traffic loads of the two types of services, the proposed scheme dynamically partitions and allocates the random access channel (RACH) resource to each type of services. The RACH partition strategy is thoroughly optimized to increase the access performances of M2M networks. Analyses and
simulation demonstrate the effectiveness of our design. The proposed scheme can outperform the baseline access class barring (ACB) scheme, which ignores service types in access control, in terms of access success probability and the average access delay
Phase-resolved ocean wave forecast with simultaneous current estimation through data assimilation
In Wang & Pan (J. Fluid Mech., vol. 918, A19, 2021), the authors developed
the first ensemble-based data assimilation (DA) capability for the
reconstruction and forecast of ocean surface waves, namely the EnKF-HOS method
coupling an ensemble Kalman filter (EnKF) and the high-order spectral (HOS)
method. In this work, we continue to enrich the method by allowing it to
simultaneously estimate the ocean current field, which is in general not known
a priori and can (slowly) vary in both space and time. To achieve this goal, we
incorporate the effect of ocean current (as unknown parameters) on waves to
build the HOS-C method as the forward prediction model, and obtain a
simultaneous estimation of (current) parameters and (wave) states via an
iterative EnKF (IEnKF) method that is necessary to handle the complexity in
this DA problem. The new algorithm, named IEnKF-HOS-C method, is first tested
in synthetic problems with various forms (steady/unsteady, uniform/non-uniform)
of current. It is shown that the IEnKF-HOS-C method is able to not only
estimate the current field accurately, but also boost the prediction accuracy
of the wave field (even) relative to the state-of-the-art EnKF-HOS method.
Finally, using real data from a shipborne radar, we show that the IEnKF-HOS-C
method successfully recovers the current speed that matches the in situ
measurement by a floating buoy
ColdNAS: Search to Modulate for User Cold-Start Recommendation
Making personalized recommendation for cold-start users, who only have a few
interaction histories, is a challenging problem in recommendation systems.
Recent works leverage hypernetworks to directly map user interaction histories
to user-specific parameters, which are then used to modulate predictor by
feature-wise linear modulation function. These works obtain the
state-of-the-art performance. However, the physical meaning of scaling and
shifting in recommendation data is unclear. Instead of using a fixed modulation
function and deciding modulation position by expertise, we propose a modulation
framework called ColdNAS for user cold-start problem, where we look for proper
modulation structure, including function and position, via neural architecture
search. We design a search space which covers broad models and theoretically
prove that this search space can be transformed to a much smaller space,
enabling an efficient and robust one-shot search algorithm. Extensive
experimental results on benchmark datasets show that ColdNAS consistently
performs the best. We observe that different modulation functions lead to the
best performance on different datasets, which validates the necessity of
designing a searching-based method
HS-Diffusion: Learning a Semantic-Guided Diffusion Model for Head Swapping
Image-based head swapping task aims to stitch a source head to another source
body flawlessly. This seldom-studied task faces two major challenges: 1)
Preserving the head and body from various sources while generating a seamless
transition region. 2) No paired head swapping dataset and benchmark so far. In
this paper, we propose an image-based head swapping framework (HS-Diffusion)
which consists of a semantic-guided latent diffusion model (SG-LDM) and a
semantic layout generator. We blend the semantic layouts of source head and
source body, and then inpaint the transition region by the semantic layout
generator, achieving a coarse-grained head swapping. SG-LDM can further
implement fine-grained head swapping with the blended layout as condition by a
progressive fusion process, while preserving source head and source body with
high-quality reconstruction. To this end, we design a head-cover augmentation
strategy for training and a neck alignment trick for geometric realism.
Importantly, we construct a new image-based head swapping benchmark and propose
two tailor-designed metrics (Mask-FID and Focal-FID). Extensive experiments
demonstrate the superiority of our framework. The code will be available:
https://github.com/qinghew/HS-Diffusion
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