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Article: Polymorphs of Nb

Pang, Rui / Wang, Zhiqiang / Li, Jinkai / Chen, Kunfeng

2023 Volume 16, Issue 21

Abstract: Niobium pentoxide ( ... ...

Abstract	Niobium pentoxide (Nb
Language	English
Publishing date	2023-10-30
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2487261-1
ISSN	1996-1944
ISSN	1996-1944
DOI	10.3390/ma16216956
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: An extended time-varying Budyko framework for quantifying the hydrological effect of vegetation restoration under climate variations at watershed scale.

Zhang, Yifan / Pang, Jianzhuang / Xu, Hang / Leng, Manman / Zhang, Zhiqiang

Environmental research

2024 Volume 251, Issue Pt 2, Page(s) 118730

Abstract: The Budyko framework, widely used to quantify the watershed hydrological response to the watershed characteristics and climate variabilities, is continuously refined to overcome the disadvantages of steady state assumption. However, dynamic variations in ...

Abstract	The Budyko framework, widely used to quantify the watershed hydrological response to the watershed characteristics and climate variabilities, is continuously refined to overcome the disadvantages of steady state assumption. However, dynamic variations in vegetations and climate variables are not fully integrated including coverages and precipitation regimes of intensity, frequency, and duration. To address this, we developed an innovative approach for determining the parameter ω in the Budyko framework to quantify the hydrological effects of vegetation restoration in a mesoscale watershed located in northern China. We found that fractional vegetation coverage (FVC), heavy precipitation amount (95pTOT), and the number of precipitation days (R01mm) are significant variables for estimating ω to improve the predictive capability of the watershed response. This extended time-varying Budyko framework can rigorously capture the temporal variations and underlying mechanisms of interactions between vegetation dynamic and precipitation regime partitioning precipitation (P) to R. Under the Budyko-Fu framework, compared to constant ω (ω‾) or ω that only considers FVC (ω
Language	English
Publishing date	2024-03-15
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	205699-9
ISSN	1096-0953 ; 0013-9351
ISSN (online)	1096-0953
ISSN	0013-9351
DOI	10.1016/j.envres.2024.118730
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Article: A newly-recorded species of the genus

Xu, Huixin / Pang, Jiaxin / Li, Jing / Cheng, Zhiqiang

Biodiversity data journal

2022 Volume 10, Page(s) e96740

Abstract: Background: The genus : New information: ... ...

Abstract	Background: The genus New information: Rhodotritomamanipurica
Language	English
Publishing date	2022-12-19
Publishing country	Bulgaria
Document type	Journal Article
ZDB-ID	2736709-5
ISSN	1314-2828
ISSN	1314-2828
DOI	10.3897/BDJ.10.e96740
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Article: Differences in Characteristics and Outcomes Between Large-Cell Neuroendocrine Carcinoma of the Ovary and High-Grade Serous Ovarian Cancer: A Retrospective Observational Cohort Study.

Pang, Li / Guo, Zhiqiang

Frontiers in oncology

2022 Volume 12, Page(s) 891699

Abstract: Background: Owing to its extremely low incidence and the paucity of relevant reports, there is currently no recognized first-line treatment strategy for ovarian large-cell neuroendocrine carcinoma, and there are no statistics related to prognosis ... ...

Abstract	Background: Owing to its extremely low incidence and the paucity of relevant reports, there is currently no recognized first-line treatment strategy for ovarian large-cell neuroendocrine carcinoma, and there are no statistics related to prognosis derived from large samples. This study aimed to investigate the characteristics, outcomes, and independent predictors of survival for ovarian large-cell neuroendocrine carcinoma and compare them with those of high-grade serous ovarian cancer. Methods: The Surveillance, Epidemiology, and End Results database was used to identify women diagnosed with ovarian large-cell neuroendocrine carcinoma or high-grade serous ovarian cancer from 1988 to 2015. Clinical, demographic, and treatment characteristics were compared between the groups. Propensity-score matching, Cox risk regression analysis, and Kaplan-Meier survival curves were used to analyze the data. Results: In total, 23,917 women, including 23,698 (99.1%) diagnosed with high-grade serous ovarian cancer and 219 (0.9%) diagnosed with ovarian large-cell neuroendocrine carcinoma, were identified. Age >77 years, diagnosis before 2003-2010, and advanced-stage disease were more common in patients with ovarian large-cell neuroendocrine carcinoma than in those with high-grade serous ovarian cancer. Women with ovarian large-cell neuroendocrine carcinoma were less likely to receive adjuvant chemotherapy (54.8% vs. 81.9%) but more likely to receive radiotherapy (3.2% vs. 1.5%; both P<0.001) than women with high-grade serous ovarian cancer. Stage, chemotherapy, and tumor size were independent predictors of overall survival, and the risk of death was greater in the advanced stage than in the early stage (P=0.047). Chemotherapy and tumor size were also independent predictors of cancer-specific survival. Overall and cancer-specific survival rates were significantly low for ovarian large-cell neuroendocrine carcinoma than for more malignant high-grade serous ovarian cancer. Conclusions: Compared to patients with high-grade serous ovarian cancer, those with ovarian large-cell neuroendocrine carcinoma presented more often with advanced-stage disease and had decreased overall and cancer-specific survival rates.
Language	English
Publishing date	2022-05-04
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2649216-7
ISSN	2234-943X
ISSN	2234-943X
DOI	10.3389/fonc.2022.891699
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Article ; Online: Counting manatee aggregations using deep neural networks and Anisotropic Gaussian Kernel.

Wang, Zhiqiang / Pang, Yiran / Ulus, Cihan / Zhu, Xingquan

Scientific reports

2023 Volume 13, Issue 1, Page(s) 19793

Abstract: Manatees are aquatic mammals with voracious appetites. They rely on sea grass as the main food source, and often spend up to eight hours a day grazing. They move slow and frequently stay in groups (i.e. aggregations) in shallow water to search for food, ... ...

Abstract	Manatees are aquatic mammals with voracious appetites. They rely on sea grass as the main food source, and often spend up to eight hours a day grazing. They move slow and frequently stay in groups (i.e. aggregations) in shallow water to search for food, making them vulnerable to environment change and other risks. Accurate counting manatee aggregations within a region is not only biologically meaningful in observing their habit, but also crucial for designing safety rules for boaters, divers, etc., as well as scheduling nursing, intervention, and other plans. In this paper, we propose a deep learning based crowd counting approach to automatically count number of manatees within a region, by using low quality images as input. Because manatees have unique shape and they often stay in shallow water in groups, water surface reflection, occlusion, camouflage etc. making it difficult to accurately count manatee numbers. To address the challenges, we propose to use Anisotropic Gaussian Kernel (AGK), with tunable rotation and variances, to ensure that density functions can maximally capture shapes of individual manatees in different aggregations. After that, we apply AGK kernel to different types of deep neural networks primarily designed for crowd counting, including VGG, SANet, Congested Scene Recognition network (CSRNet), MARUNet etc. to learn manatee densities and calculate number of manatees in the scene. By using generic low quality images extracted from surveillance videos, our experiment results and comparison show that AGK kernel based manatee counting achieves minimum Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The proposed method works particularly well for counting manatee aggregations in environments with complex background.
Language	English
Publishing date	2023-11-13
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-45507-3
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Robust small area estimation for unit level model with density power divergence.

Niu, Xijuan / Pang, Zhiqiang / Wang, Zhaoxu

PloS one

2023 Volume 18, Issue 11, Page(s) e0288639

Abstract: Unit level model is one of the classical models in small area estimation, which plays an important role with unit information data. Empirical Bayesian(EB) estimation, as the optimal estimation under normal assumption, is the most commonly used parameter ... ...

Abstract	Unit level model is one of the classical models in small area estimation, which plays an important role with unit information data. Empirical Bayesian(EB) estimation, as the optimal estimation under normal assumption, is the most commonly used parameter estimation method in unit level model. However, this kind of method is sensitive to outliers, and EB estimation will lead to considerable inflation of the mean square error(MSE) when there are outliers in the responses yij. In this study, we propose a robust estimation method for the unit-level model with outliers based on the minimum density power divergence. Firstly, by introducing the minimum density power divergence function, we give the estimation equation of the parameters of the unit level model, and obtain the asymptotic distribution of the robust parameters. Considering the existence of tuning parameters in the robust estimator, an optimal parameter selection algorithm is proposed. Secondly, empirical Bayesian predictors of unit and area mean in finite populations are given, and the MSE of the proposed robust estimators of small area means is given by bootstrap method. Finally, we verify the superior performance of our proposed method through simulation data and real data. Through comparison, our proposed method can can solve the outlier situation better.
MeSH term(s)	Bayes Theorem ; Computer Simulation ; Algorithms ; Insufflation
Language	English
Publishing date	2023-11-16
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.0288639
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Article ; Online: Contactless Sensing-Aided Respiration Signal Acquisition Using Improved Empirical Wavelet Transform for Rhythm Detection.

Yu, Baoxian / Hou, Yue / Pang, Zhiqiang / Zhang, Han

IEEE journal of biomedical and health informatics

2023 Volume 27, Issue 7, Page(s) 3141–3151

Abstract: Respiration is one of the most important vital signs indicating physical condition, while the signal detection is challenging due to the complex rhythm and effort in practical scenarios. In this paper, we propose a contactless sensing-aided respiration ... ...

Abstract	Respiration is one of the most important vital signs indicating physical condition, while the signal detection is challenging due to the complex rhythm and effort in practical scenarios. In this paper, we propose a contactless sensing-aided respiration signal acquisition technique, which can adaptively extract the desired signal under time-varying respiration rhythms within a wide range. To be specific, respiration is perceived by piezoelectric ceramics sensors along with ballistocardiography and other interference in a contactless manner, and the proposed improved empirical wavelet transform (IEWT) performs spectrum division and recognition based on upper envelop and principal component criteria, respectively, to adaptively extract the respiration spectrum for signal reconstruction. For validations, we extracted respiration signals from 8 healthy individuals in lab breathing at specified rhythms from 0.2 Hz to 0.6 Hz as well as 38 in-patients suffering from sleep-disordered-breathing with reference of polysomnogram in practical clinic scenario. The results showed that the detected respiration rhythms perfectly fitted the ones in experimental lab dataset with a correlation coefficient of 0.98, which validated the effectiveness of the respiration spectrum extraction of the proposed IEWT method. Besides, in practical clinical dataset, the proposed IEWT method could yield mean absolute and relative errors of respiration intervals of 0.4 and 0.05 seconds, respectively, achieving significant improvement in comparison with conventional ones. Meanwhile, the performance of IEWT was robust to rhythm variation, individual difference and breathing cycle detection techniques, which demonstrated the feasibility and superiority of the proposed IEWT method for practical respiration monitoring.
MeSH term(s)	Humans ; Wavelet Analysis ; Respiration ; Vital Signs ; Sleep Apnea Syndromes ; Polysomnography ; Signal Processing, Computer-Assisted ; Algorithms
Language	English
Publishing date	2023-06-30
Publishing country	United States
Document type	Journal Article
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2023.3271349
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Article ; Online: Exploring Spillover Effects for COVID-19 Cascade Prediction.

Chen, Ninghan / Chen, Xihui / Zhong, Zhiqiang / Pang, Jun

Entropy (Basel, Switzerland)

2022 Volume 24, Issue 2

Abstract: An information outbreak occurs on social media along with the COVID-19 pandemic and leads to ... ...

Abstract	An information outbreak occurs on social media along with the COVID-19 pandemic and leads to an
Language	English
Publishing date	2022-01-31
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/e24020222
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Article ; Online: Coupled pyrite and sulfur autotrophic denitrification for simultaneous removal of nitrogen and phosphorus from secondary effluent: feasibility, performance and mechanisms.

Chen, Zhiqiang / Pang, Chao / Wen, Qinxue

Water research

2023 Volume 243, Page(s) 120422

Abstract: The discharge standards of nitrogen (N) and phosphorus (P) in wastewater treatment plants (WWTPs) have become increasingly strict to reduce water eutrophication. Further reducing N and P in effluent from municipal WWTPs need to be achieved effectively ... ...

Abstract	The discharge standards of nitrogen (N) and phosphorus (P) in wastewater treatment plants (WWTPs) have become increasingly strict to reduce water eutrophication. Further reducing N and P in effluent from municipal WWTPs need to be achieved effectively and eco-friendly. In this study, a carbon independent pyrite and sulfur autotrophic denitrification (PSAD) system using pyrite and sulfur as electron donor was developed and compared with pyrite autotrophic denitrification (PAD) and sulfur autotrophic denitrification (SAD) systems through batch and continuous flow biofilter experiments. Compare to PAD and SAD, PSAD was more effective in simultaneous removal in N and P. At hydraulic retention time (HRT) 3 h, average effluent concentrations of total nitrogen (TN) and total phosphate (TP) of 1.40 ± 0.03 and 0.19 ± 0.02 mg/L were achieved when treating real secondary effluent with 20.65 ± 0.24 mg/L TN and 1.00 ± 0.24 mg/L TP. The improvement in simultaneous removal of N and P was attributed to the coupling of PAD and SAD in enhancing the transformation of sulfur and iron and enlarging the reaction zone in the pyrite and sulfur autotrophic denitrification biofilter (PSADB) system. Therefore, more biomass was accumulated and the microbial denitrification functional stability, including electrons transfer and consumption was enhanced on the surface of pyrite and sulfur particles in the PSADB system. Moreover, autotrophic denitrifiers (Thiobacillus and Ferritrophicum), sulfate-reducing bacteria (Desulfocapsa) and iron reducing bacteria (Geothrix), acting as contributors to microbial nitrogen, sulfur and iron cycle, were specially enriched. In addition, the leaching of iron ions was promoted, which facilitated the removal of phosphate in the form of Fe
MeSH term(s)	Phosphorus ; Nitrogen ; Denitrification ; Feasibility Studies ; Bioreactors ; Nitrates ; Iron ; Sulfur ; Phosphates ; Autotrophic Processes
Chemical Substances	pyrite (132N09W4PR) ; Phosphorus (27YLU75U4W) ; Nitrogen (N762921K75) ; Nitrates ; Iron (E1UOL152H7) ; Sulfur (70FD1KFU70) ; Phosphates
Language	English
Publishing date	2023-07-27
Publishing country	England
Document type	Journal Article
ZDB-ID	202613-2
ISSN	1879-2448 ; 0043-1354
ISSN (online)	1879-2448
ISSN	0043-1354
DOI	10.1016/j.watres.2023.120422
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Article ; Online: Robust small area estimation for unit level model with density power divergence.

Xijuan Niu / Zhiqiang Pang / Zhaoxu Wang

PLoS ONE, Vol 18, Iss 11, p e

2023 Volume 0288639

Abstract	Unit level model is one of the classical models in small area estimation, which plays an important role with unit information data. Empirical Bayesian(EB) estimation, as the optimal estimation under normal assumption, is the most commonly used parameter estimation method in unit level model. However, this kind of method is sensitive to outliers, and EB estimation will lead to considerable inflation of the mean square error(MSE) when there are outliers in the responses yij. In this study, we propose a robust estimation method for the unit-level model with outliers based on the minimum density power divergence. Firstly, by introducing the minimum density power divergence function, we give the estimation equation of the parameters of the unit level model, and obtain the asymptotic distribution of the robust parameters. Considering the existence of tuning parameters in the robust estimator, an optimal parameter selection algorithm is proposed. Secondly, empirical Bayesian predictors of unit and area mean in finite populations are given, and the MSE of the proposed robust estimators of small area means is given by bootstrap method. Finally, we verify the superior performance of our proposed method through simulation data and real data. Through comparison, our proposed method can can solve the outlier situation better.
Keywords	Medicine ; R ; Science ; Q
Subject code	310
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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