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Article: Editorial: Case Reports in Pediatric Rheumatology 2022.

Bakhtiari Koohsorkhi, Maryam / Wu, Junfeng / Ziaee, Vahid

2023 Volume 11, Page(s) 1137843

Language	English
Publishing date	2023-02-06
Publishing country	Switzerland
Document type	Editorial
ZDB-ID	2711999-3
ISSN	2296-2360
ISSN	2296-2360
DOI	10.3389/fped.2023.1137843
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: GraphTS: Graph-represented time series for subsequence anomaly detection.

Zarei, Roozbeh / Huang, Guangyan / Wu, Junfeng

PloS one

2023 Volume 18, Issue 8, Page(s) e0290092

Abstract: Automatic detection of subsequence anomalies (i.e., an abnormal waveform denoted by a sequence of data points) in time series is critical in a wide variety of domains. However, most existing methods for subsequence anomaly detection often require knowing ...

Abstract	Automatic detection of subsequence anomalies (i.e., an abnormal waveform denoted by a sequence of data points) in time series is critical in a wide variety of domains. However, most existing methods for subsequence anomaly detection often require knowing the length and the total number of anomalies in time series. Some methods fail to capture recurrent subsequence anomalies due to using only local or neighborhood information for anomaly detection. To address these limitations, in this paper, we propose a novel graph-represented time series (GraphTS) method for discovering subsequence anomalies. In GraphTS, we provide a new concept of time series graph representation model, which represents both recurrent and rare patterns in a time series. Particularly, in GraphTS, we develop a new 2D time series visualization (2Dviz) method, which compacts all 1D time series patterns into a 2D spatial temporal space. The 2Dviz method transfers time series patterns into a higher-resolution plot for easier sequence anomaly recognition (or detecting subsequence anomalies). Then, a Graph is constructed based on the 2D spatial temporal space of time series to capture recurrent and rare subsequence patterns effectively. The represented Graph also can be used to discover single and recurrent subsequence anomalies with arbitrary lengths. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods in terms of accuracy and efficiency.
MeSH term(s)	Time Factors ; Recognition, Psychology
Language	English
Publishing date	2023-08-16
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0290092
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Article ; Online: A Multiple-Medical-Image Encryption Method Based on SHA-256 and DNA Encoding.

Wu, Junfeng / Zhang, Jialu / Liu, Dong / Wang, Xiaofeng

Entropy (Basel, Switzerland)

2023 Volume 25, Issue 6

Abstract: Ensuring the privacy and secrecy of digital medical images has become a pressing issue as a result of the quick development of smart medical technology and the exponential growth in the quantity of medical images transmitted and stored in networks. The ... ...

Abstract	Ensuring the privacy and secrecy of digital medical images has become a pressing issue as a result of the quick development of smart medical technology and the exponential growth in the quantity of medical images transmitted and stored in networks. The lightweight multiple-image encryption approach for medical images that is suggested in this research can encrypt/decrypt any number of medical photos of varied sizes with just one encryption operation and has a computational cost that is similar to encrypting a single image. The plaintext images with different sizes are filled at the right and bottom of the image to ensure that the size of all plaintext images is uniform; then, all the filled images are stacked to obtain a superimposed image. The initial key, which is generated using the SHA-256 technique, is then used as the starting value of the linear congruence algorithm to create the encryption key sequence. The cipher picture is then created by encrypting the superimposed image with the encryption key and DNA encoding. The algorithm can be made even more secure by implementing a decryption mechanism that decrypts the image independently in order to reduce the possibility of information leaking during the decryption process. The outcomes of the simulation experiment demonstrate the algorithm's strong security and resistance to interference such as noise pollution and lost image content.
Language	English
Publishing date	2023-06-03
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2014734-X
ISSN	1099-4300 ; 1099-4300
ISSN (online)	1099-4300
ISSN	1099-4300
DOI	10.3390/e25060898
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Article: The safety and efficacy of mini-percutaneous nephrolithotomy

Wu, Junfeng / Shen, Jun

Translational andrology and urology

2021 Volume 10, Issue 4, Page(s) 1734–1742

Abstract: Background: To compare the safety and efficacy of mini-percutaneous nephrolithotomy (PCNL) and retrograde intrarenal surgery (RIRS) for the treatment of renal lithiasis in patients with pelvic ectopic kidney.: Methods: From January 2015 to October ... ...

Abstract	Background: To compare the safety and efficacy of mini-percutaneous nephrolithotomy (PCNL) and retrograde intrarenal surgery (RIRS) for the treatment of renal lithiasis in patients with pelvic ectopic kidney. Methods: From January 2015 to October 2017, mini-PCNL and RIRS were performed in ten patients diagnosed with lithiasis in pelvic ectopic kidneys, including three cases under laparoscopy-assisted mini-PCNL. Patient demographics and perioperative characteristics (age, gender, BMI, side of pelvic kidney, stone size, stone number, stone location, special medical history, and ASA physical status classification), and operative and post-operative related details (operation time, hospital stay, blood loss, VAS, analgesic requirement, complications, and stone free outcome) were reviewed. Results: Although the mean operation time of mini-PCNL (71.3 min) was shorter than RIRS (85.3 min), the mean operation time of laparoscopy assisted mini-PCNL (92 min) was longer than patients without laparoscopy-assisted mini-PCNL (55.8 min). However, the use of mini-PCNL allowed for larger lithiasis to be dealt with (1.9 cm in laparoscopy assisted mini-PCNL and 2.4 cm in mini-PCNL without laparoscopy-assist) compared with RIRS (1.2 cm). In addition, although the mean hospital-stay time, blood loss, and analgesic requirement of patients undergoing RIRS were less than those receiving mini-PCNL, the success rate of RIRS was only 50% (3/6) in comparison to 100% (7/7) for mini-PCNL. Except for pain and urinary tract infection after the operation, there were no significant intraoperative and postoperative complications, and no residual lithiasis were seen in any patient. Conclusions: Although RIRS was less time-consuming and invasive, mini-PCNL can deal with the bigger lithiasis and more complex situations with a higher success rate. Both mini-PCNL and RIRS are feasible and safe treatments for pelvic ectopic kidney lithiasis with each carrying unique advantages. Hence in practice, an appropriate individualized treatment should be selected depending on patient characteristics.
Language	English
Publishing date	2021-04-04
Publishing country	China
Document type	Journal Article
ZDB-ID	2851630-8
ISSN	2223-4691 ; 2223-4691 ; 2223-4683
ISSN (online)	2223-4691
ISSN	2223-4691 ; 2223-4683
DOI	10.21037/tau-21-77
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Article ; Online: Words that hurt: Leaders' anti-Asian communication and employee outcomes.

Jun, Sora / Wu, Junfeng

The Journal of applied psychology

2021 Volume 106, Issue 2, Page(s) 169–184

Abstract: With the outbreak of COVID-19, there have been growing reports of racial harassment targeting Asian Americans. We study one such manifestation of racial harassment that Asian employees may face in the workplace: Leaders' use of stigmatizing labels for ... ...

Abstract	With the outbreak of COVID-19, there have been growing reports of racial harassment targeting Asian Americans. We study one such manifestation of racial harassment that Asian employees may face in the workplace: Leaders' use of stigmatizing labels for COVID-19 such as the "Chinese Virus" and "Kung Flu." Integrating organizational justice theories with research on racial harassment in the workplace, we theorize that leaders' use of stigmatizing COVID-19 labels reduces employees' perceptions of interpersonal justice, which subsequently impact employees' emotional exhaustion and work engagement. We further theorize that while such effects will be stronger among Asian employees who experience both moral anger and reduced public collective self-esteem, that the effects will also be present among non-Asian employees who experience moral anger. Using one survey (Study 1) and one experiment (Study 2), we find support for our predictions. We find that leaders' use of stigmatizing language to depict COVID-19 leads to deleterious workplace experiences for employees, and especially for Asian employees. The current research thus deepens our understanding of the relatively understudied work experiences of Asian Americans and brings to light the underlying psychological mechanisms linking racial harassment and employee work outcomes for both targeted employees and employees not targeted. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
MeSH term(s)	Adult ; Asian Americans/psychology ; COVID-19 ; Communication ; Female ; Humans ; Leadership ; Male ; Middle Aged ; Morals ; Organizational Culture ; Racism/psychology ; SARS-CoV-2 ; Social Justice ; Social Stigma ; Surveys and Questionnaires ; Workplace/psychology ; Young Adult
Language	English
Publishing date	2021-04-05
Publishing country	United States
Document type	Journal Article
ZDB-ID	219157-x
ISSN	1939-1854 ; 0021-9010
ISSN (online)	1939-1854
ISSN	0021-9010
DOI	10.1037/apl0000873
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Article ; Online: GUGA-based MRCI approach with core-valence separation approximation (CVS) for the calculation of the core-excited states of molecules.

Song, Qi / Liu, Baoyuan / Wu, Junfeng / Zou, Wenli / Wang, Yubin / Suo, Bingbing / Lei, Yibo

The Journal of chemical physics

2024 Volume 160, Issue 9

Abstract: We develop and demonstrate how to use the Graphical Unitary Group Approach (GUGA)-based MRCISD with Core-Valence Separation (CVS) approximation to compute the core-excited states. First, perform a normal Self-Consistent-Field (SCF) or valence MCSCF ... ...

Abstract	We develop and demonstrate how to use the Graphical Unitary Group Approach (GUGA)-based MRCISD with Core-Valence Separation (CVS) approximation to compute the core-excited states. First, perform a normal Self-Consistent-Field (SCF) or valence MCSCF calculation to optimize the molecular orbitals. Second, rotate the optimized target core orbitals and append to the active space, form an extended CVS active space, and perform a CVS-MCSCF calculation for core-excited states. Finally, construct the CVS-MRCISD expansion space and perform a CVS-MRCISD calculation to optimize the CI coefficients based on the variational method. The CVS approximation with GUGA-based methods can be implemented by flexible truncation of the Distinct Row Table. Eliminating the valence-excited configurations from the CVS-MRCISD expansion space can prevent variational collapse in the Davidson iteration diagonalization. The accuracy of the CVS-MRCISD scheme was investigated for excitation energies and compared with that of the CVS-MCSCF and CVS-CASPT2 methods using the same active space. The results show that CVS-MRCISD is capable of reproducing well-matched vertical core excitation energies that are consistent with experiments by combining large basis sets and a rational reference space. The calculation results also highlight the fact that the dynamic correlation between electrons makes an undeniable contribution in core-excited states.
Language	English
Publishing date	2024-03-06
Publishing country	United States
Document type	Journal Article
ZDB-ID	3113-6
ISSN	1089-7690 ; 0021-9606
ISSN (online)	1089-7690
ISSN	0021-9606
DOI	10.1063/5.0189443
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: LIFRNet: A Novel Lightweight Individual Fish Recognition Method Based on Deformable Convolution and Edge Feature Learning

Yin, Jianhao / Wu, Junfeng / Gao, Chunqi / Jiang, Zhongai

Agriculture. 2022 Nov. 22, v. 12, no. 12

2022

Abstract: With the continuous development of industrial aquaculture and artificial intelligence technology, the trend of the use of automation and intelligence in aquaculture is becoming more and more obvious, and the speed of the related technical development is ... ...

Abstract	With the continuous development of industrial aquaculture and artificial intelligence technology, the trend of the use of automation and intelligence in aquaculture is becoming more and more obvious, and the speed of the related technical development is becoming faster and faster. Individual fish recognition could provide key technical support for fish growth monitoring, bait feeding and density estimation, and also provide strong data support for fish precision farming. However, individual fish recognition faces significant hurdles due to the underwater environment complexity, high visual similarity of individual fish and the real-time aspect of the process. In particular, the complex and changeable underwater environment makes it extremely difficult to detect individual fish and extract biological features extraction. In view of the above problems, this paper proposes an individual fish recognition method based on lightweight convolutional neural network (LIFRNet). This proposed method could extract the visual features of underwater moving fish accurately and efficiently and give each fish unique identity recognition information. The method proposed in this paper consists of three parts: the underwater fish detection module, underwater individual fish recognition module and result visualization module. In order to improve the accuracy and real-time availability of recognition, this paper proposes a lightweight backbone network for fish visual feature extraction. This research constructed a dataset for individual fish recognition (DlouFish), and the fish in dataset were manually sorted and labeled. The dataset contains 6950 picture information instances of 384 individual fish. In this research, simulation experiments were carried out on the DlouFish dataset. Compared with YOLOV4-Tiny and YOLOV4, the accuracy of the proposed method in fish detection was increased by 5.12% and 3.65%, respectively. Additionally, the accuracy of individual fish recognition reached 97.8%.
Keywords	agriculture ; animal growth ; aquaculture ; artificial intelligence ; automation ; data collection ; fish ; neural networks
Language	English
Dates of publication	2022-1122
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article ; Online
ZDB-ID	2651678-0
ISSN	2077-0472
ISSN	2077-0472
DOI	10.3390/agriculture12121972
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Statistical Interior Tomography via L

Wu, Junfeng / Wang, Xiaofeng / Mou, Xuanqin

Tomography (Ann Arbor, Mich.)

2022 Volume 8, Issue 5, Page(s) 2218–2231

Abstract: Interior tomography of X-ray computed tomography (CT) has many advantages, such as a lower radiation dose and lower detector hardware cost compared to traditional CT. However, this imaging technique only uses the projection data passing through the ... ...

Abstract	Interior tomography of X-ray computed tomography (CT) has many advantages, such as a lower radiation dose and lower detector hardware cost compared to traditional CT. However, this imaging technique only uses the projection data passing through the region of interest (ROI) for imaging; accordingly, the projection data are truncated at both ends of the detector, so the traditional analytical reconstruction algorithm cannot satisfy the demand of clinical diagnosis. To solve the above limitations, in this paper we propose a high-quality statistical iterative reconstruction algorithm that uses the zeroth-order image moment as novel prior knowledge; the zeroth-order image moment can be estimated in the projection domain using the Helgason-Ludwig consistency condition. Then, the L
MeSH term(s)	Algorithms ; Artifacts ; Image Processing, Computer-Assisted/methods ; Phantoms, Imaging ; Tomography, X-Ray Computed/methods
Language	English
Publishing date	2022-09-02
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2379-139X
ISSN (online)	2379-139X
DOI	10.3390/tomography8050186
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Article ; Online: TDLAS second harmonic demodulation based on Hilbert transform.

Wu, Junfeng / Chen, Hanyu / Kang, Guohua / Li, Xu

PloS one

2022 Volume 17, Issue 12, Page(s) e0278724

Abstract: A demodulation method for tunable diode laser absorption spectroscopy (TDLAS) second harmonic based on the Hilbert transform is proposed in this paper. The second harmonic of the TDLAS signal can be easily obtained without a reference signal. The TDLAS ... ...

Abstract	A demodulation method for tunable diode laser absorption spectroscopy (TDLAS) second harmonic based on the Hilbert transform is proposed in this paper. The second harmonic of the TDLAS signal can be easily obtained without a reference signal. The TDLAS signal is firstly processed by band-pass filtering, then the envelope of the processed signal is obtained with Hilbert transform. And finally, the second harmonic is extracted from the 1f component of the envelope. The validity of the proposed method is confirmed by simulation and verified by experiment, and the result shows that the error is acceptable under the cases of weak absorbance, which indicates that the proposed method is applicable to practical trace gas detection.
Language	English
Publishing date	2022-12-05
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0278724
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Article ; Online: Analysis of risk correlations among stock markets during the COVID-19 pandemic.

Wu, JunFeng / Zhang, Chao / Chen, Yun

International review of financial analysis

2022 Volume 83, Page(s) 102220

Abstract: The outbreak of the COVID-19 pandemic significantly negatively impacted the global economy and stock markets. This paper investigates the stock-market tail risks caused by the COVID-19 pandemic and how the pandemic affects the risk correlations among the ...

Abstract	The outbreak of the COVID-19 pandemic significantly negatively impacted the global economy and stock markets. This paper investigates the stock-market tail risks caused by the COVID-19 pandemic and how the pandemic affects the risk correlations among the stock markets worldwide. The conditional autoregressive value at risk (CAViaR) model is used to measure the tail risks of 28 selected stock markets. Furthermore, risk correlation networks are constructed to describe the risk correlations among stock markets during different periods. Through dynamic analysis of the risk correlations, the influence of the COVID-19 pandemic on stock markets worldwide is examined quantitatively. The results show the following: (i) The COVID-19 pandemic has caused significant tail risks in stock markets in most countries, while the stock markets of a few countries have been unaffected by the pandemic. (ii) The topology of risk correlation networks has become denser during the COVID-19 pandemic. The impact of the COVID-19 pandemic makes it easier for risk to transfer among stock markets. (iii) The increase in the closeness of the risk relationship between countries with lower economic correlation has become much higher than that between counties with higher economic correlation during the COVID-19 pandemic. For researchers and policy-makers, these findings reveal practical implications of the risk correlations among stock markets.
Language	English
Publishing date	2022-06-03
Publishing country	United States
Document type	Journal Article
ISSN	1873-8079
ISSN (online)	1873-8079
DOI	10.1016/j.irfa.2022.102220
Database	MEDical Literature Analysis and Retrieval System OnLINE

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