5,838 research outputs found
Oxidation of triethylene glycol and tetraethylene glycol by ditelluratocuprate(III) in alkaline medium - A kinetic and mechanistic study
The kinetics of oxidation of triethylene glycol (TEG) and tetraethylene glycol (TTEG) by ditelluratocuprate(III) (DTC) in alkaline liquids were investigated spectrophotometrically in the temperature range of 20oC to 40oC. It was found that the reaction followed pseudo-first order in DTC and less than unit order in reductants. The rate constant kobs of pseudo-first order reaction decreased with an increase of [TeO42-], whereas adding [OH-] enhanced the constant. In addition, there was a negative salt effect. A suitable assumption involving pre-equilibriums before the rate controlling step and a free radical mechanism was proposed from the kinetics study. The rate equations derived from mechanism can explain all experimental phenomena. Moreover, the activation parameters at 298.2K and rate constants of the rate-determining step were evaluated
Fast solvers for tridiagonal Toeplitz linear systems
Let A be a tridiagonal Toeplitz matrix denoted by A=Tritoep(Ī²,Ī±,Ī³). The matrix A is said to be: strictly diagonally dominant if |Ī±|>|Ī²|+|Ī³|, weakly diagonally dominant if |Ī±|ā„|Ī²|+|Ī³|, subdiagonally dominant if |Ī²|ā„|Ī±|+|Ī³|, and superdiagonally dominant if |Ī³|ā„|Ī±|+|Ī²|. In this paper, we consider the solution of a tridiagonal Toeplitz system Ax=b, where A is subdiagonally dominant, superdiagonally dominant, or weakly diagonally dominant, respectively. We first consider the case of A being subdiagonally dominant. We transform A into a block 2Ć2 matrix by an elementary transformation and then solve such a linear system using the block LU factorization. Compared with the LU factorization method with pivoting, our algorithm takes less flops, and needs less memory storage and data transmission. In particular, our algorithm outperforms the LU factorization method with pivoting in terms of computing efficiency. Then, we deal with superdiagonally dominant and weakly diagonally dominant cases, respectively. Numerical experiments are finally given to illustrate the effectiveness of our algorithmsNational Natural Science Foundation of China under Grant no. 11371075, the Hunan Key Laboratory of mathematical modeling and analysis in engineering, the research innovation program of Changsha University of Science and Technology for postgraduate students under Grant (CX2019SS34), and the Portuguese Funds through FCT-FundaĆ§Ć£o para a CiĆŖncia, within the Project UIDB/00013/2020 and UIDP/00013/202
Detection of the third and fourth heart sounds using Hilbert-Huang transform
<p>Abstract</p> <p>Background</p> <p>The third and fourth heart sound (S3 and S4) are two abnormal heart sound components which are proved to be indicators of heart failure during diastolic period. The combination of using diastolic heart sounds with the standard ECG as a measurement of ventricular dysfunction may improve the noninvasive diagnosis and early detection of myocardial ischemia.</p> <p>Methods</p> <p>In this paper, an adaptive method based on time-frequency analysis is proposed to detect the presence of S3 and S4. Heart sound signals during diastolic periods were analyzed with Hilbert-Huang Transform (HHT). A discrete plot of maximal instantaneous frequency and its amplitude was generated and clustered. S3 and S4 were recognized by the clustered points, and performance of the method was further enhanced by period definition and iteration tracking.</p> <p>Results</p> <p>Using the proposed method, S3 and S4 could be detected adaptively in a same method. 90.3% of heart sound cycles with S3 were detected using our method, 9.6% were missed, and 9.6% were false positive. 94% of S4 were detected using our method, 5.5% were missed, and 16% were false positive.</p> <p>Conclusions</p> <p>The proposed method is adaptive for detecting low-amplitude and low-frequency S3 and S4 simultaneously compared with previous detection methods, which would be practical in primary care.</p
CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization
Low-dose computed tomography (CT) images suffer from noise and artifacts due
to photon starvation and electronic noise. Recently, some works have attempted
to use diffusion models to address the over-smoothness and training instability
encountered by previous deep-learning-based denoising models. However,
diffusion models suffer from long inference times due to the large number of
sampling steps involved. Very recently, cold diffusion model generalizes
classical diffusion models and has greater flexibility. Inspired by the cold
diffusion, this paper presents a novel COntextual eRror-modulated gEneralized
Diffusion model for low-dose CT (LDCT) denoising, termed CoreDiff. First,
CoreDiff utilizes LDCT images to displace the random Gaussian noise and employs
a novel mean-preserving degradation operator to mimic the physical process of
CT degradation, significantly reducing sampling steps thanks to the informative
LDCT images as the starting point of the sampling process. Second, to alleviate
the error accumulation problem caused by the imperfect restoration operator in
the sampling process, we propose a novel ContextuaL Error-modulAted Restoration
Network (CLEAR-Net), which can leverage contextual information to constrain the
sampling process from structural distortion and modulate time step embedding
features for better alignment with the input at the next time step. Third, to
rapidly generalize to a new, unseen dose level with as few resources as
possible, we devise a one-shot learning framework to make CoreDiff generalize
faster and better using only a single LDCT image (un)paired with NDCT.
Extensive experimental results on two datasets demonstrate that our CoreDiff
outperforms competing methods in denoising and generalization performance, with
a clinically acceptable inference time. Source code is made available at
https://github.com/qgao21/CoreDiff.Comment: IEEE Transactions on Medical Imaging, 202
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