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Article ; Online: Transport and behavior of marine oil spill containing polycyclic aromatic hydrocarbons in mesocosm experiments

Shi, Dawei / Jia, Houlei

J. Ocean. Limnol.. 2023 Jan., v. 41, no. 1 p.166-173

2023

Abstract: Polycyclic aromatic hydrocarbons (PAHs) are one of the most important groups in oil, and responsible for major toxic and/or carcinogenic impact on humans and wildlife. It is important to understand the behavior of PAHs in marine environment after an oil- ... ...

Abstract	Polycyclic aromatic hydrocarbons (PAHs) are one of the most important groups in oil, and responsible for major toxic and/or carcinogenic impact on humans and wildlife. It is important to understand the behavior of PAHs in marine environment after an oil-spill incident. However, interaction between petroleum PAHs and microbial communities in a marine environment remains unclear. Therefore, a series of mesocosm experiments were conducted, in which water-accommodated fraction (WAF) of oil was generated to simulate an oil-spill scenario and to analyze the transport and behavior of marine oil spill containing PAHs with and without dispersants. Results indicate that the application of dispersant could increase the concentration of total PAHs in water column due mainly to significant increase in the concentration of high-molecular weight (HMW) PAHs at a lower removal rate. At the end of the 7-day experiment, significant amount of HMW PAHs were accumulated in sediment. In general, the application of dispersant did not increase the sediment uptake of PAHs but increased the PAHs concentration in water column.
Keywords	carcinogenicity ; dispersants ; marine environment ; oil spills ; oils ; petroleum ; sediments ; wildlife
Language	English
Dates of publication	2023-01
Size	p. 166-173.
Publishing place	Science Press
Document type	Article ; Online
ISSN	2096-5508
DOI	10.1007/s00343-022-1388-7
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: Innovation-triggered Learning for Data-driven Predictive Control

Zheng, Kaikai / Shi, Dawei / Hirche, Sandra / Shi, Yang

Deterministic and Stochastic Formulations

2024

Abstract: Data-driven control has attracted lots of attention in recent years, especially for plants that are difficult to model based on first-principle. In particular, a key issue in data-driven approaches is how to make efficient use of data as the abundance of ...

Abstract	Data-driven control has attracted lots of attention in recent years, especially for plants that are difficult to model based on first-principle. In particular, a key issue in data-driven approaches is how to make efficient use of data as the abundance of data becomes overwhelming. {To address this issue, this work proposes an innovation-triggered learning framework and a corresponding data-driven controller design approach with guaranteed stability. Specifically, we consider a linear time-invariant system with unknown dynamics subject to deterministic/stochastic disturbances, respectively. Two kinds of data selection mechanisms are proposed by online evaluating the innovation contained in the sampled data, wherein the innovation is quantified by its effect of shrinking the set of potential system dynamics that are compatible with the sampled data. Next, after introducing a stability criterion using the set-valued estimation of system dynamics, a robust data-driven predictive controller is designed by minimizing a worst-case cost function.} The closed-loop stability of the data-driven predictive controller equipped with the innovation-triggered learning protocol is proved with a high probability framework. Finally, numerical experiments are performed to verify the validity of the proposed approaches, and the characteristics and the selection principle of the learning hyper-parameter are also discussed.
Keywords	Electrical Engineering and Systems Science - Systems and Control
Subject code	629
Publishing date	2024-01-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A Pipeline Leakage Signal Simulation and Generation System

Shi Dawei / Zhao Hongliang / Shao Xiuwen

E3S Web of Conferences, Vol 257, p

2021 Volume 03070

Abstract: To test the effectiveness of the detection and positioning technology of the pipe leakage, the propagation law of pipeline leakage signal is studied in this paper, and a pipeline leakage signal simulation and generation system is proposed. It can ... ...

Abstract	To test the effectiveness of the detection and positioning technology of the pipe leakage, the propagation law of pipeline leakage signal is studied in this paper, and a pipeline leakage signal simulation and generation system is proposed. It can simulate the leakage pressure wave signals at different positions of the pipeline. Changing pipe’s parameters though the computer, the simulation and output of the leakage signal under various working conditions can be realized. It can test the reliability and accuracy of the detection and location technology of the pipe leakage, and verify the applicability of the pipe leakage detection and location technology to different pipe structures. The results show that the output signal of system can replace the real signal, and located the pre-set leakage point by cross-correlation method. The purpose of studying the effectiveness and accuracy of the existing leak location algorithm base on largescale complex pipe network system in laboratory conditions was realized.
Keywords	Environmental sciences ; GE1-350
Subject code	621
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	EDP Sciences
Document type	Article ; Online
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Article ; Online: Linezolid and the risk of QT interval prolongation: A pharmacovigilance study of the Food and Drug Administration Adverse Event Reporting System.

Shao, Haixia / Shi, Dawei / Dai, Ying

British journal of clinical pharmacology

2022 Volume 89, Issue 4, Page(s) 1386–1392

Abstract: Aims: Few studies have investigated linezolid (LZD)-associated cardiotoxicity. This study explored the potential association between LZD and QT interval prolongation.: Methods: Adverse event reports of QT interval prolongation associated with LZD ... ...

Abstract	Aims: Few studies have investigated linezolid (LZD)-associated cardiotoxicity. This study explored the potential association between LZD and QT interval prolongation. Methods: Adverse event reports of QT interval prolongation associated with LZD from the Food and Drug Administration Adverse Event Reporting System from January 2013 to December 2021 were analysed and the reporting odds ratio (ROR) with 95% confidence intervals were calculated. Results: A total of 6738 adverse event reports of LZD as the primary and secondary suspected drug were obtained from the database, including 192 reports with electrocardiogram QT prolonged (QTp), and the ROR value was 26.1 (95% CI = 22.6-30.2). There were 8 reports of long QT syndrome, ROR 14.2 (95% CI = 7.1-28.5); 5 reports of torsade de pointes, ROR 3.2 (95% CI = 1.3-7.6); and 5 reports of ventricular tachycardia, ROR 1.9 (95% CI = 0.8-4.5). Subgroup analysis revealed that patients with tuberculosis treated with LZD had a higher reporting rate among all QTp reports, exhibiting an odds ratio of 330.0 (95% CI = 223.1-488.1). The odds ratios of QTp associated with LZD treatments in patients with and without tuberculosis were 4.2 (95% CI = 3.4-5.3) and 1.2 (95% CI = 0.8-1.6), respectively. Conclusion: The study showed an association between LZD and QT interval prolongation. In the report on patients with tuberculosis, the incidence of QTp was higher when treated with LZD.
MeSH term(s)	United States/epidemiology ; Humans ; Linezolid/adverse effects ; Pharmacovigilance ; United States Food and Drug Administration ; Long QT Syndrome/chemically induced ; Long QT Syndrome/epidemiology ; Torsades de Pointes
Chemical Substances	Linezolid (ISQ9I6J12J)
Language	English
Publishing date	2022-11-23
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	188974-6
ISSN	1365-2125 ; 0306-5251 ; 0264-3774
ISSN (online)	1365-2125
ISSN	0306-5251 ; 0264-3774
DOI	10.1111/bcp.15587
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Article ; Online: Tf

Shi, Da-Wei / Yue, Hui-Qi / Li, Ming / Liu, Jie / Wang, Chang-Cheng / Yang, Shang-Dong / Yang, Bin

The Journal of organic chemistry

2024

Abstract: We have developed a ... ...

Abstract	We have developed a Tf
Language	English
Publishing date	2024-05-01
Publishing country	United States
Document type	Journal Article
ZDB-ID	123490-0
ISSN	1520-6904 ; 0022-3263
ISSN (online)	1520-6904
ISSN	0022-3263
DOI	10.1021/acs.joc.3c02970
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Article ; Online: Rapid classification of SARS-CoV-2 variant strains using machine learning-based label-free SERS strategy.

Qin, Jingwang / Tian, Xiangdong / Liu, Siying / Yang, Zhengxia / Shi, Dawei / Xu, Sihong / Zhang, Yun

Talanta

2023 Volume 267, Page(s) 125080

Abstract: The spread of COVID-19 over the past three years is largely due to the continuous mutation of the virus, which has significantly impeded global efforts to prevent and control this epidemic. Specifically, mutations in the amino acid sequence of the ... ...

Abstract	The spread of COVID-19 over the past three years is largely due to the continuous mutation of the virus, which has significantly impeded global efforts to prevent and control this epidemic. Specifically, mutations in the amino acid sequence of the surface spike (S) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have directly impacted its biological functions, leading to enhanced transmission and triggering an immune escape effect. Therefore, prompt identification of these mutations is crucial for formulating targeted treatment plans and implementing precise prevention and control measures. In this study, the label-free surface-enhanced Raman scattering (SERS) technology combined with machine learning (ML) algorithms provide a potential solution for accurate identification of SARS-CoV-2 variants. We establish a SERS spectral database of SARS-CoV-2 variants and demonstrate that a diagnostic classifier using a logistic regression (LR) algorithm can provide accurate results within 10 min. Our classifier achieves 100% accuracy for Beta (B.1.351/501Y.V2), Delta (B.1.617), Wuhan (COVID-19) and Omicron (BA.1) variants. In addition, our method achieves 100% accuracy in blind tests of positive and negative human nasal swabs based on the LR model. This method enables detection and classification of variants in complex biological samples. Therefore, ML-based SERS technology is expected to accurately discriminate various SARS-CoV-2 variants and may be used for rapid diagnosis and therapeutic decision-making.
MeSH term(s)	Humans ; SARS-CoV-2/genetics ; COVID-19/diagnosis ; Algorithms ; Machine Learning
Language	English
Publishing date	2023-08-17
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1500969-5
ISSN	1873-3573 ; 0039-9140
ISSN (online)	1873-3573
ISSN	0039-9140
DOI	10.1016/j.talanta.2023.125080
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Article ; Online: A dual-attention based coupling network for diabetes classification with heterogeneous data.

Wang, Lei / Pan, Zhenglin / Liu, Wei / Wang, Junzheng / Ji, Linong / Shi, Dawei

Journal of biomedical informatics

2023 Volume 139, Page(s) 104300

Abstract: Diabetes Mellitus (DM) is a group of metabolic disorders characterized by hyperglycaemia in the absence of treatment. Classification of DM is essential as it corresponds to the respective diagnosis and treatment. In this paper, we propose a new coupling ... ...

Abstract	Diabetes Mellitus (DM) is a group of metabolic disorders characterized by hyperglycaemia in the absence of treatment. Classification of DM is essential as it corresponds to the respective diagnosis and treatment. In this paper, we propose a new coupling network with hierarchical dual-attention that utilizes heterogeneous data, including Flash Glucose Monitoring (FGM) data and biomarkers in electronic medical records. The long short-term memory-based FGM sub-network extracts the time-dependent features of dynamic FGM sequences, while the biomarkers sub-network learns the features of static biomarkers. The convolutional block attention module (CBAM) for dispersing the feature weights of the spatial and channel dimensions is implemented into the FGM sub-network to endure the variability of FGM and allows us to extract high-level discriminative features more accurately. To better adjust the importance weights of the characteristics of the two sub-networks, self-attention is introduced to integrate the characteristics of heterogeneous data. Based on the dataset provided by Peking University People's Hospital, the proposed method is evaluated through factorial experiments of multi-source heterogeneous data, ablation studies of various attention strategies, time consumption evaluation and quantitative evaluation. The benchmark tests reveal the proposed network achieves a type 1 and 2 diabetes classification accuracy of 95.835% and the comprehensive performance metrics, including Matthews correlation coefficient, F1-score and G-mean, are 91.333%, 94.939% and 94.937% respectively. In the factorial experiments, the proposed method reaches the maximum area under the receiver operating characteristic curve of 0.9428, which indicates the effectiveness of the coupling between the nominated sub-networks. The coupling network with a dual-attention strategy performs better than the one without or only with a single-attention strategy in the ablation study as well. In addition, the model is also tested on another data set, and the accuracy of the test reaches 94.286%, reflecting that the model is robust when it is transferred to untrained diabetes data. The experimental results show that the proposed method is feasible in the classification of diabetes types. The code is available at https://github.com/bitDalei/Diabetes-Classification-with-Heterogeneous-Data.
MeSH term(s)	Humans ; Blood Glucose Self-Monitoring ; Diabetes Mellitus, Type 1 ; Blood Glucose ; Diabetes Mellitus, Type 2 ; Benchmarking
Chemical Substances	Blood Glucose
Language	English
Publishing date	2023-02-02
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2057141-0
ISSN	1532-0480 ; 1532-0464
ISSN (online)	1532-0480
ISSN	1532-0464
DOI	10.1016/j.jbi.2023.104300
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Book ; Online: Generalized Multi-kernel Maximum Correntropy Kalman Filter for Disturbance Estimation

Li, Shilei / Shi, Dawei / Lou, Yunjiang / Zou, Wulin / Shi, Ling

2023

Abstract: Disturbance observers have been attracting continuing research efforts and are widely used in many applications. Among them, the Kalman filter-based disturbance observer is an attractive one since it estimates both the state and the disturbance ... ...

Abstract	Disturbance observers have been attracting continuing research efforts and are widely used in many applications. Among them, the Kalman filter-based disturbance observer is an attractive one since it estimates both the state and the disturbance simultaneously, and is optimal for a linear system with Gaussian noises. Unfortunately, The noise in the disturbance channel typically exhibits a heavy-tailed distribution because the nominal disturbance dynamics usually do not align with the practical ones. To handle this issue, we propose a generalized multi-kernel maximum correntropy Kalman filter for disturbance estimation, which is less conservative by adopting different kernel bandwidths for different channels and exhibits excellent performance both with and without external disturbance. The convergence of the fixed point iteration and the complexity of the proposed algorithm are given. Simulations on a robotic manipulator reveal that the proposed algorithm is very efficient in disturbance estimation with moderate algorithm complexity. Comment: in IEEE Transactions on Automatic Control (2023)
Keywords	Electrical Engineering and Systems Science - Systems and Control
Subject code	629
Publishing date	2023-10-30
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Multi-kernel Correntropy-based Orientation Estimation of IMUs

Li, Shilei / Li, Lijing / Shi, Dawei / Lou, Yunjiang / Shi, Ling

Gradient Descent Methods

2023

Abstract: This paper presents two computationally efficient algorithms for the orientation estimation of inertial measurement units (IMUs): the correntropy-based gradient descent (CGD) and the correntropy-based decoupled orientation estimation (CDOE). Traditional ... ...

Abstract	This paper presents two computationally efficient algorithms for the orientation estimation of inertial measurement units (IMUs): the correntropy-based gradient descent (CGD) and the correntropy-based decoupled orientation estimation (CDOE). Traditional methods, such as gradient descent (GD) and decoupled orientation estimation (DOE), rely on the mean squared error (MSE) criterion, making them vulnerable to external acceleration and magnetic interference. To address this issue, we demonstrate that the multi-kernel correntropy loss (MKCL) is an optimal objective function for maximum likelihood estimation (MLE) when the noise follows a type of heavy-tailed distribution. In certain situations, the estimation error of the MKCL is bounded even in the presence of arbitrarily large outliers. By replacing the standard MSE cost function with MKCL, we develop the CGD and CDOE algorithms. We evaluate the effectiveness of our proposed methods by comparing them with existing algorithms in various situations. Experimental results indicate that our proposed methods (CGD and CDOE) outperform their conventional counterparts (GD and DOE), especially when faced with external acceleration and magnetic disturbances. Furthermore, the new algorithms demonstrate significantly lower computational complexity than Kalman filter-based approaches, making them suitable for applications with low-cost microprocessors. Comment: 16 pages
Keywords	Electrical Engineering and Systems Science - Systems and Control ; Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Signal Processing
Publishing date	2023-04-13
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Multi-kernel Correntropy Regression

Li, Shilei / Lou, Yunjiang / Shi, Dawei / Li, Lijing / Shi, Ling

Robustness, Optimality, and Application on Magnetometer Calibration

2023

Abstract: This paper investigates the robustness and optimality of the multi-kernel correntropy (MKC) on linear regression. We first derive an upper error bound for a scalar regression problem in the presence of arbitrarily large outliers and reveal that the ... ...

Abstract	This paper investigates the robustness and optimality of the multi-kernel correntropy (MKC) on linear regression. We first derive an upper error bound for a scalar regression problem in the presence of arbitrarily large outliers and reveal that the kernel bandwidth should be neither too small nor too big in the sense of the lowest upper error bound. Meanwhile, we find that the proposed MKC is related to a specific heavy-tail distribution, and the level of the heavy tail is controlled by the kernel bandwidth solely. Interestingly, this distribution becomes the Gaussian distribution when the bandwidth is set to be infinite, which allows one to tackle both Gaussian and non-Gaussian problems. We propose an expectation-maximization (EM) algorithm to estimate the parameter vectors and explore the kernel bandwidths alternatively. The results show that our algorithm is equivalent to the traditional linear regression under Gaussian noise and outperforms the conventional method under heavy-tailed noise. Both numerical simulations and experiments on a magnetometer calibration application verify the effectiveness of the proposed method.
Keywords	Electrical Engineering and Systems Science - Systems and Control
Subject code	519
Publishing date	2023-04-13
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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