Up to now several gravitational-wave events from the coalescences of black hole binaries have been reported by LIGO/VIRGO, and imply that black holes should have an extended mass function. We work ...out the merger rate distribution of primordial black hole (PBH) binaries with a general mass function by taking into account the torques by all PBHs and linear density perturbations. In the future, many more coalescences of black hole binaries are expected to be detected, and the one-dimensional and two-dimensional merger rate distributions will be crucial for reconstructing the mass function of PBHs.
Two algorithms based on machine learning neural networks are proposed—the shallow learning (S‐L) and deep learning (D‐L) algorithms—that can potentially be used in atmosphere‐only typhoon forecast ...models to provide flow‐dependent typhoon‐induced sea surface temperature cooling (SSTC) for improving typhoon predictions. The major challenge of existing SSTC algorithms in forecast models is how to accurately predict SSTC induced by an upcoming typhoon, which requires information not only from historical data but more importantly also from the target typhoon itself. The S‐L algorithm composes of a single layer of neurons with mixed atmospheric and oceanic factors. Such a structure is found to be unable to represent correctly the physical typhoon‐ocean interaction. It tends to produce an unstable SSTC distribution, for which any perturbations may lead to changes in both SSTC pattern and strength. The D‐L algorithm extends the neural network to a 4 × 5 neuron matrix with atmospheric and oceanic factors being separated in different layers of neurons, so that the machine learning can determine the roles of atmospheric and oceanic factors in shaping the SSTC. Therefore, it produces a stable crescent‐shaped SSTC distribution, with its large‐scale pattern determined mainly by atmospheric factors (e.g., winds) and small‐scale features by oceanic factors (e.g., eddies). Sensitivity experiments reveal that the D‐L algorithms improve maximum wind intensity errors by 60–70% for four case study simulations, compared to their atmosphere‐only model runs.
Plain Language Summary
Forecasting accuracy with respect to storm track and intensity are two important factors for evaluating typhoon models. While 24‐h forecast errors of typhoon track have steadily improved to an order of 50 km, the prediction of typhoon intensity has remained one of the major challenges during the last decade. In this study, two algorithms based on machine‐learning neural networks are proposed‐the shallow learning (S‐L) and deep learning (D‐L) algorithms‐that can potentially be used in atmosphere‐only typhoon forecast models to provide flow‐dependent typhoon‐induced sea surface temperature cooling (SSTC) for improving typhoon predictions.
Key Points
A parameterization scheme based on deep learning neural network is proposed for atmosphere‐only typhoon forecast models
The deep learning algorithm is designed to combine information from historical data and the target typhoon
The scheme based on the deep learning algorithm achieves an equivalent representation as the fully coupled model
The advent of gravitational-wave and multimessenger astronomy has stimulated research on the formation mechanisms of binary black holes (BBHs) observed by the Laser Interferometer Gravitational-Wave ...Observatory (LIGO)/Virgo. In the literature, the progenitors of these BBHs could be stellar-origin black holes (sBHs) or primordial black holes (PBHs). In this paper, we calculate the stochastic gravitational-wave background (SGWB) from BBHs, covering the astrophysical and primordial scenarios separately, together with the one from binary neutron stars (BNSs). Our results indicate that PBHs contribute a stronger SGWB than that from sBHs, and the total SGWB from both BBHs and BNSs has a high possibility of being detected by the future observing runs of LIGO/Virgo and the Laser Interferometer Space Antenna (LISA). On the other hand, the SGWB from BBHs and BNSs also contributes an additional source of confusion noise to LISA's total noise curve, and then weakens LISA's detection abilities. For instance, the detection of massive black hole binary (MBHB) coalescences is one of the key missions of LISA, and the largest detectable redshift of MBHB mergers can be significantly reduced.
XGBoost Model for Chronic Kidney Disease Diagnosis Ogunleye, Adeola; Wang, Qing-Guo
IEEE/ACM transactions on computational biology and bioinformatics,
2020-Nov.-Dec.-1, 2020 Nov-Dec, 2020-11-1, 20201101, Volume:
17, Issue:
6
Journal Article
Peer reviewed
Chronic Kidney Disease (CKD) is a menace that is affecting 10 percent of the world population and 15 percent of the South African population. The early and cheap diagnosis of this disease with ...accuracy and reliability will save 20,000 lives in South Africa per year. Scientists are developing smart solutions with Artificial Intelligence (AI). In this paper, several typical and recent AI algorithms are studied in the context of CKD and the extreme gradient boosting (XGBoost) is chosen as our base model for its high performance. Then, the model is optimized and the optimal full model trained on all the features achieves a testing accuracy, sensitivity, and specificity of 1.000, 1.000, and 1.000, respectively. Note that, to cover the widest range of people, the time and monetary costs of CKD diagnosis have to be minimized with fewest patient tests. Thus, the reduced model using fewer features is desirable while it should still maintain high performance. To this end, the set-theory based rule is presented which combines a few feature selection methods with their collective strengths. The reduced model using about a half of the original full features performs better than the models based on individual feature selection methods and achieves accuracy, sensitivity and specificity, of 1.000, 1.000, and 1.000, respectively.
Intelligent bearing fault diagnosis based on deep learning is one of the hotspots in mechanical equipment monitoring applications. However, traditional deep learning-based methods have a weak ...antinoise ability and poor generalization performance in a noisy environment. This article presents a new simple and effective deep attention mechanism network, namely, dual-path mixed-domain residual threshold network (DP-MRTN), which aims to improve the accuracy of the rolling bearing fault diagnosis in a noisy environment. The DP-MRTN combines the channel attention mechanism, spatial attention mechanism, and residual structure. The soft threshold function is used as the nonlinear transformation layer, and the dilated convolution is introduced to establish a dual-path neural network so as to select the important features in the signal without resorting to any signal denoising algorithm. The performance of the DP-MRTN is validated against those state-of-the-art results on the real three-phase asynchronous motor experiment platform in Zhejiang University of Technology. We have achieved 99.97<inline-formula><tex-math notation="LaTeX">\%</tex-math></inline-formula> (<inline-formula><tex-math notation="LaTeX"> \pm 0.09\%</tex-math></inline-formula>) accuracy on Gaussian white noise, 99.87<inline-formula><tex-math notation="LaTeX">\%</tex-math></inline-formula> (<inline-formula><tex-math notation="LaTeX"> \pm 0.12\%</tex-math></inline-formula>) accuracy on Laplacian noise, and 99.98<inline-formula><tex-math notation="LaTeX">\%</tex-math></inline-formula> (<inline-formula><tex-math notation="LaTeX"> \pm 0.02\%</tex-math></inline-formula>) accuracy on real noise. The results show that the proposed method can significantly improve the accuracy of fault diagnosis in a noisy environment compared with the traditional deep learning method.
Photocatalysis has been widely applied in various areas, such as solar cells, water splitting, and pollutant degradation. Therefore, the photochemical mechanisms and basic principles of ...photocatalysis, especially TiO2 photocatalysis, have been extensively investigated by various surface science methods in the last decade, aiming to provide important information for TiO2 photocatalysis under real environmental conditions. Recent progress that provides fundamental insights into TiO2 photocatalysis at a molecular level is highlighted. Insights into the structures of TiO2 and the basic principles of TiO2 photocatalysis are discussed first, which provides the basic concepts of TiO2 photocatalysis. Following this, details of the photochemistry of three important molecules (oxygen, water, methanol) on the model TiO2 surfaces are presented, in an attempt to unravel the relationship between charge/energy transfer and bond breaking/forming in TiO2 photocatalysis. Lastly, challenges and opportunities of the mechanistic studies of TiO2 photocatalysis at the molecular level are discussed briefly, as well as possible photocatalysis models.
The basic principles and fundamental processes of TiO2 photocatalysis are highlighted. Recent progress made on the studies of the nature of TiO2 photocatalysis, in particular whether photocatalytic reactions are driven by separated charges or by energy produced via nonadiabatic exciton decay or nonadiabatic charge recombination, is summarized and discussed in detail.
As the cyber-attack is becoming one of the most challenging threats faced by cyber-physical systems, investigating the effect of cyber-attacks on distributed optimization and designing resilient ...algorithms are of both theoretical merits and practical values. Most existing works are established on the assumption that the maximum tolerable number of attacks, which depends on the network connectivity, is known by all normal agents. All normal agents will use the maximum number of attacks to decide whether the received information will be used for iterations. In this article, we relax this assumption and propose a novel resilient distributed optimization algorithm. The proposed algorithm exploits the trusted agents which cannot be compromised by adversarial attacks and form a connected dominating set in the original graph to constrain effects of adversarial attacks. It is shown that local variables of all normal and trusted agents converge to the same value under the proposed algorithm. Further, the final solution belongs to the convex set of minimizers of the weighted average of local cost functions of all trusted agents. The upper bound of the distance between the final solution and the optimal one has also been provided. Numerical results are presented to demonstrate the effectiveness of the proposed algorithm.
The antimalarial drug artemisinin and its derivatives have been explored as potential anticancer agents, but their underlying mechanisms are controversial. In this study, we found that artemisinin ...compounds can sensitize cancer cells to ferroptosis, a new form of programmed cell death driven by iron-dependent lipid peroxidation. Mechanistically, dihydroartemisinin (DAT) can induce lysosomal degradation of ferritin in an autophagy-independent manner, increasing the cellular free iron level and causing cells to become more sensitive to ferroptosis. Further, by associating with cellular free iron and thus stimulating the binding of iron-regulatory proteins (IRPs) with mRNA molecules containing iron-responsive element (IRE) sequences, DAT impinges on IRP/IRE-controlled iron homeostasis to further increase cellular free iron. Importantly, in both in vitro and a mouse xenograft model in which ferroptosis was triggered in cancer cells by the inducible knockout of GPX4, we found that DAT can augment GPX4 inhibition-induced ferroptosis in a cohort of cancer cells that are otherwise highly resistant to ferroptosis. Collectively, artemisinin compounds can sensitize cells to ferroptosis by regulating cellular iron homeostasis. Our findings can be exploited clinically to enhance the effect of future ferroptosis-inducing cancer therapies.
This article is concerned with the output-feedback tracking control problem for a class of nonlinear systems. The challenge lies in how to drive the tracking error to a prescribed region in a given ...time globally, under both unknown nonlinear functions and unknown virtual control coefficients. To deal with the unknown control direction, an orientation function is exploited in lieu of the Nussbaum gain technique, the supervisory switching strategy, and the parameter estimation approach. To attain global tracking performance, a tuning function is employed to adjust the tracking error, and a barrier function is used to combat this error. The resulting controller guarantees the prescribed tracking performance and the boundedness of the signals in the closed loop for any initial condition.
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