437 research outputs found
Autogenous Volume Deformation of Hydraulic Concrete
AbstractIn hydraulic mass concrete construction, the autogenous volume deformation is a more important factor for concrete to generate adverse tensile stress, which will lead to structural cracks. The adverse effect of autogenous volume deformation of concrete will be offset by cooling pipe skills. That is, to make the volume deformation unchangeable or minimum after pouring, the autogenous volume deformation is set to be counteracted by moderate temperature expansion deformation. The simulation results show that the adverse effect of autogenous volume shrinkage deformation of concrete can decrease obviously by controlling cooling water during construction period. The results can provide certain references to hydraulic mass concrete rapid construction
Adaptive smoothness constraint image multilevel fuzzy enhancement algorithm
For the problems of poor enhancement effect and long time consuming of the traditional algorithm, an adaptive smoothness constraint image multilevel fuzzy enhancement algorithm based on secondary color-to-grayscale conversion is proposed. By using fuzzy set theory and generalized fuzzy set theory, a new linear generalized fuzzy operator transformation is carried out to obtain a new linear generalized fuzzy operator. By using linear generalized membership transformation and inverse transformation, secondary color-to-grayscale conversion of adaptive smoothness constraint image is performed. Combined with generalized fuzzy operator, the region contrast fuzzy enhancement of adaptive smoothness constraint image is realized, and image multilevel fuzzy enhancement is realized. Experimental results show that the fuzzy degree of the image is reduced by the improved algorithm, and the clarity of the adaptive smoothness constraint image is improved effectively. The time consuming is short, and it has some advantages
GWAI: Harnessing Artificial Intelligence for Enhancing Gravitational Wave Data Analysis
Gravitational wave (GW) astronomy has opened new frontiers in understanding
the cosmos, while the integration of artificial intelligence (AI) in science
promises to revolutionize data analysis methodologies. However, a significant
gap exists, as there is currently no dedicated platform that enables scientists
to develop, test, and evaluate AI algorithms efficiently. To address this gap,
we introduce GWAI, a pioneering AI-centered software platform designed for
gravitational wave data analysis. GWAI contains a three-layered architecture
that emphasizes simplicity, modularity, and flexibility, covering the entire
analysis pipeline. GWAI aims to accelerate scientific discoveries, bridging the
gap between advanced AI techniques and astrophysical research.Comment: 10 pages, 5 figure
Compact Binary Systems Waveform Generation with Generative Pre-trained Transformer
Space-based gravitational wave detection is one of the most anticipated
gravitational wave (GW) detection projects in the next decade, which will
detect abundant compact binary systems. However, the precise prediction of
space GW waveforms remains unexplored. To solve the data processing difficulty
in the increasing waveform complexity caused by detectors' response and
second-generation time-delay interferometry (TDI 2.0), an interpretable
pre-trained large model named CBS-GPT (Compact Binary Systems Waveform
Generation with Generative Pre-trained Transformer) is proposed. For compact
binary system waveforms, three models were trained to predict the waveforms of
massive black hole binary (MBHB), extreme mass-ratio inspirals (EMRIs), and
galactic binary (GB), achieving prediction accuracies of 98%, 91%, and 99%,
respectively. The CBS-GPT model exhibits notable interpretability, with its
hidden parameters effectively capturing the intricate information of waveforms,
even with complex instrument response and a wide parameter range. Our research
demonstrates the potential of large pre-trained models in gravitational wave
data processing, opening up new opportunities for future tasks such as gap
completion, GW signal detection, and signal noise reduction
DECODE: DilatEd COnvolutional neural network for Detecting Extreme-mass-ratio inspirals
The detection of Extreme Mass Ratio Inspirals (EMRIs) is intricate due to
their complex waveforms, extended duration, and low signal-to-noise ratio
(SNR), making them more challenging to be identified compared to compact binary
coalescences. While matched filtering-based techniques are known for their
computational demands, existing deep learning-based methods primarily handle
time-domain data and are often constrained by data duration and SNR. In
addition, most existing work ignores time-delay interferometry (TDI) and
applies the long-wavelength approximation in detector response calculations,
thus limiting their ability to handle laser frequency noise. In this study, we
introduce DECODE, an end-to-end model focusing on EMRI signal detection by
sequence modeling in the frequency domain. Centered around a dilated causal
convolutional neural network, trained on synthetic data considering TDI-1.5
detector response, DECODE can efficiently process a year's worth of
multichannel TDI data with an SNR of around 50. We evaluate our model on 1-year
data with accumulated SNR ranging from 50 to 120 and achieve a true positive
rate of 96.3% at a false positive rate of 1%, keeping an inference time of less
than 0.01 seconds. With the visualization of three showcased EMRI signals for
interpretability and generalization, DECODE exhibits strong potential for
future space-based gravitational wave data analyses.Comment: 13 pages, 5 figures, and 2 table
Dawning of a New Era in Gravitational Wave Data Analysis: Unveiling Cosmic Mysteries via Artificial Intelligence -- A Systematic Review
Background: Artificial intelligence (AI), with its vast capabilities, has
become an integral part of our daily interactions, particularly with the rise
of sophisticated models like Large Language Models. These advancements have not
only transformed human-machine interactions but have also paved the way for
significant breakthroughs in various scientific domains. Aim of review: This
review is centered on elucidating the profound impact of AI, especially deep
learning, in the field of gravitational wave data analysis (GWDA). We aim to
highlight the challenges faced by traditional GWDA methodologies and how AI
emerges as a beacon of hope, promising enhanced accuracy, real-time processing,
and adaptability. Key scientific concepts of review: Gravitational wave (GW)
waveform modeling stands as a cornerstone in the realm of GW research, serving
as a sophisticated method to simulate and interpret the intricate patterns and
signatures of these cosmic phenomena. This modeling provides a deep
understanding of the astrophysical events that produce gravitational waves.
Next in line is GW signal detection, a refined technique that meticulously
combs through extensive datasets, distinguishing genuine gravitational wave
signals from the cacophony of background noise. This detection process is
pivotal in ensuring the authenticity of observed events. Complementing this is
the GW parameter estimation, a method intricately designed to decode the
detected signals, extracting crucial parameters that offer insights into the
properties and origins of the waves. Lastly, the integration of AI for GW
science has emerged as a transformative force. AI methodologies harness vast
computational power and advanced algorithms to enhance the efficiency,
accuracy, and adaptability of data analysis in GW research, heralding a new era
of innovation and discovery in the field
Insecticidal Activity of the Whole Grass Extract of Typha angustifolia and its Active Component against Solenopsis invicta
In this study, the toxicity of whole grass Typha angustifolia L. extract was determined in vitro by a “water tube” method to investigate the bioactivity of T. angustifolia L. against micrergates of red imported fire ants. Results indicated that the ethanol extract exhibited toxicity against the micrergates of red imported fire ants. Mortality was 100% after the micrergates were treated with 2000 mg/mL of ethanol extract for 72 h. After 48 h of treatment, LC50 values of ethanol extract and petroleum ether fraction were 956.85 and 398.73 mg/mL, respectively. After 120 h, LC50 values of the same substances were 271.23 and 152.86 mg/mL, respectively. A bioactivity-guided fractionation and chemical investigation of petroleum ether fraction yielded an active component (compound 1). NMR spectra revealed that the structure of compound 1 corresponded to 3β-hydroxy-25-methylenecycloartan-24-ol. Compound 1 also exhibited strong toxicity against the micrergates of red imported fire ants, thereby eradicating all of the tested ants treated with 240 mg/mL for 120 h. LC50 values of compound 1 at 48 and 120 h were 316.50 and 28.52 mg/mL, respectively
Self-attention based high order sequence feature reconstruction of dynamic functional connectivity networks with rs-fMRI for brain disease classification
Dynamic functional connectivity networks (dFCN) based on rs-fMRI have
demonstrated tremendous potential for brain function analysis and brain disease
classification. Recently, studies have applied deep learning techniques (i.e.,
convolutional neural network, CNN) to dFCN classification, and achieved better
performance than the traditional machine learning methods. Nevertheless,
previous deep learning methods usually perform successive convolutional
operations on the input dFCNs to obtain high-order brain network aggregation
features, extracting them from each sliding window using a series split, which
may neglect non-linear correlations among different regions and the
sequentiality of information. Thus, important high-order sequence information
of dFCNs, which could further improve the classification performance, is
ignored in these studies. Nowadays, inspired by the great success of
Transformer in natural language processing and computer vision, some latest
work has also emerged on the application of Transformer for brain disease
diagnosis based on rs-fMRI data. Although Transformer is capable of capturing
non-linear correlations, it lacks accounting for capturing local spatial
feature patterns and modelling the temporal dimension due to parallel
computing, even equipped with a positional encoding technique. To address these
issues, we propose a self-attention (SA) based convolutional recurrent network
(SA-CRN) learning framework for brain disease classification with rs-fMRI data.
The experimental results on a public dataset (i.e., ADNI) demonstrate the
effectiveness of our proposed SA-CRN method
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