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Book ; Online: Remote Sensing of the Terrestrial Hydrologic Cycle

Tang, Qiuhong / Qi, Youcun / Wang, Zhihui / Pan, Yun

2020

Keywords	Research & information: general ; Geography ; hydrological cycle ; Three-North region ; climate change ; land cover change ; Variable Infiltration Capacity (VIC) model ; evapotranspiration ; runoff ; soil moisture ; three-temperature model ; infrared remote sensing ; urban hedges ; cooling effects ; irrigation mapping ; remote sensing ; random forest ; subhumid region ; dry-wet regime ; vegetation dynamics ; GLDAS ; GIMMS ; Yarlung Zangbo River ; Microwave emissivity difference vegetation index (EDVI) ; evapotranspiration (ET) ; satellite remote sensing ; cloudy sky ; clouds and earth's radiation energy system (CERES) ; ChinaFLUX ; precipitation classification ; K-nearest neighbor ; Doppler radar ; Tropical Precipitation Measurement Mission (TRMM) ; irrigation signal ; SMAP ; irrigation intensity ; winter wheat ; precipitation ; evaluation ; error analysis ; Fengyun ; quantitative precipitation estimates ; GPM ; IMERG ; deep learning ; Daihai Lake ; Huangqihai Lake ; lake degradation ; weather radar quantitative precipitation estimation ; rain gauge ; radar-rain gauge merging ; leave-one-out cross validation ; verification ; China ; exorheic catchments ; water balance ; GRACE ; terrestrial water storage changes ; reservoir storage ; MODIS ; SRTM ; n/a
Size	1 electronic resource (260 pages)
Publisher	MDPI - Multidisciplinary Digital Publishing Institute
Publishing place	Basel, Switzerland
Document type	Book ; Online
Note	English ; Open Access
HBZ-ID	HT021044443
ISBN	9783039288083 ; 3039288083
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article ; Online: Higher Education Management and Student Achievement Assessment Method Based on Clustering Algorithm.

Wang, Zhihui

Computational intelligence and neuroscience

2022 Volume 2022, Page(s) 4703975

Abstract: Monitoring and guiding instructional management require student performance evaluation. Traditional evaluation and analysis methods based on absolute scores, on the other hand, have certain flaws and are unable to fully reflect the information contained ... ...

Abstract	Monitoring and guiding instructional management require student performance evaluation. Traditional evaluation and analysis methods based on absolute scores, on the other hand, have certain flaws and are unable to fully reflect the information contained in student performance, thus limiting the impact of student performance evaluation on teaching and learning management. Data mining is regarded as the backbone technology for future information processing, and it introduces a new concept to the way humans use data. Schools must analyse and evaluate the performance of students in the same grade level and secondary school in a timely and staged manner. Clustering is a type of data mining that uses similarity rules to classify sample data into groups with a high degree of similarity. To address the difficulties caused by the wide variation in course difficulty in student performance evaluation, a method based on the
MeSH term(s)	Academic Success ; Algorithms ; Cluster Analysis ; Humans ; Learning ; Students
Language	English
Publishing date	2022-07-04
Publishing country	United States
Document type	Journal Article
ZDB-ID	2388208-6
ISSN	1687-5273 ; 1687-5273
ISSN (online)	1687-5273
ISSN	1687-5273
DOI	10.1155/2022/4703975
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Article ; Online: HoRDA: Learning higher-order structure information for predicting RNA-disease associations.

Li, Julong / Chen, Jianrui / Wang, Zhihui / Lei, Xiujuan

Artificial intelligence in medicine

2024 Volume 148, Page(s) 102775

Abstract: CircRNA and miRNA are crucial non-coding RNAs, which are associated with biological diseases. Exploring the associations between RNAs and diseases often requires a significant time and financial investments, which has been greatly alleviated and improved ...

Abstract	CircRNA and miRNA are crucial non-coding RNAs, which are associated with biological diseases. Exploring the associations between RNAs and diseases often requires a significant time and financial investments, which has been greatly alleviated and improved with the application of deep learning methods in bioinformatics. However, existing methods often fail to achieve higher accuracy and cannot be universal between multiple RNAs. Moreover, complex RNA-disease associations hide important higher-order topology information. To address these issues, we learn higher-order structure information for predicting RNA-disease associations (HoRDA). Firstly, the correlations between RNAs and the correlations between diseases are fully explored by combining similarity and higher-order graph attention network. Then, a higher-order graph convolutional network is constructed to aggregate neighbor information, and further obtain the representations of RNAs and diseases. Meanwhile, due to the large number of complex and variable higher-order structures in biological networks, we design a higher-order negative sampling strategy to gain more desirable negative samples. Finally, the obtained embeddings of RNAs and diseases are feed into logistic regression model to acquire the probabilities of RNA-disease associations. Diverse simulation results demonstrate the superiority of the proposed method. In the end, the case study is conducted on breast neoplasms, colorectal neoplasms, and gastric neoplasms. We validate the proposed higher-order strategies through ablative and exploratory analyses and further demonstrate the practical applicability of HoRDA. HoRDA has a certain contribution in RNA-disease association prediction.
MeSH term(s)	Humans ; Algorithms ; MicroRNAs/genetics ; Colorectal Neoplasms ; Computational Biology/methods
Chemical Substances	MicroRNAs
Language	English
Publishing date	2024-01-15
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	645179-2
ISSN	1873-2860 ; 0933-3657
ISSN (online)	1873-2860
ISSN	0933-3657
DOI	10.1016/j.artmed.2024.102775
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Article ; Online: Removal of potentially toxic elements from water with the moss-tufa micro-filtration system.

Tian, Qingrong / Zhang, Zhaohui / Wang, Zhihui

Ecotoxicology and environmental safety

2024 Volume 272, Page(s) 116039

Abstract: Mosses are an integral component in the tufa sedimentary landscape. In this study, we investigated the use of the porous moss-tufa structure as a filtration system for removing potentially toxic elements (PTEs) from water samples. Three species of mosses ...

Abstract	Mosses are an integral component in the tufa sedimentary landscape. In this study, we investigated the use of the porous moss-tufa structure as a filtration system for removing potentially toxic elements (PTEs) from water samples. Three species of mosses that commonly grow on tufa were selected, and the PTEs filtered by the moss-tufa system were identified by inductively coupled plasma mass spectrometry. The bioconcentration factor (BCF) of mosses was calculated to compare the enrichment effects of different mosses on PTEs. Likewise, the level of PTEs flowing through the moss-tufa system was measured, and the water quality removal rate (C) was calculated accordingly. The results revealed that the moss-tufa system was mainly composed of Fissidens grandifrons Brid., Hydrogonium dixonianum P. C. Chen, and Cratoneuron filicinum (Hedw.) Spruce var. filicinum. Among these, Fissidens grandifrons Brid. reported the highest retention capacity for PTEs. Collectively, the moss-tufa filtration system displayed a strong retention capacity and removal rate of Mn, Pb, and Ni from the water sample. The removal of PTEs by the moss-tufa system was mainly based on the enrichment of mosses and the adsorption-retention ability of tufa. In conclusion, the moss-tufa micro-filtration system displayed the effective removal of PTEs from water samples and could be applied to control the levels of toxic elements in karst water bodies.
MeSH term(s)	Metals, Heavy/analysis ; Environmental Monitoring/methods ; Bryophyta ; Bryopsida/chemistry ; Risk Assessment
Chemical Substances	Metals, Heavy
Language	English
Publishing date	2024-02-03
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	436536-7
ISSN	1090-2414 ; 0147-6513
ISSN (online)	1090-2414
ISSN	0147-6513
DOI	10.1016/j.ecoenv.2024.116039
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Zs.A 1418: Show issues

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Article ; Online: Higher-order neurodynamical equation for simplex prediction.

Wang, Zhihui / Chen, Jianrui / Gong, Maoguo / Shao, Zhongshi

Neural networks : the official journal of the International Neural Network Society

2024 Volume 173, Page(s) 106185

Abstract: It is demonstrated that higher-order patterns beyond pairwise relations can significantly enhance the learning capability of existing graph-based models, and simplex is one of the primary form for graphically representing higher-order patterns. ... ...

Abstract	It is demonstrated that higher-order patterns beyond pairwise relations can significantly enhance the learning capability of existing graph-based models, and simplex is one of the primary form for graphically representing higher-order patterns. Predicting unknown (disappeared) simplices in real-world complex networks can provide us with deeper insights, thereby assisting us in making better decisions. Nevertheless, previous efforts to predict simplices suffer from two issues: (i) they mainly focus on 2- or 3-simplices, and there are few models available for predicting simplices of arbitrary orders, and (ii) they lack the ability to analyze and learn the features of simplices from the perspective of dynamics. In this paper, we present a Higher-order Neurodynamical Equation for Simplex Prediction of arbitrary order (HNESP), which is a framework that combines neural networks and neurodynamics. Specifically, HNESP simulates the dynamical coupling process of nodes in simplicial complexes through different relations (i.e., strong pairwise relation, weak pairwise relation, and simplex) to learn node-level representations, while explaining the learning mechanism of neural networks from neurodynamics. To enrich the higher-order information contained in simplices, we exploit the entropy and normalized multivariate mutual information of different sub-structures of simplices to acquire simplex-level representations. Furthermore, simplex-level representations and multi-layer perceptron are used to quantify the existence probability of simplices. The effectiveness of HNESP is demonstrated by extensive simulations on seven higher-order benchmarks. Experimental results show that HNESP improves the AUC values of the state-of-the-art baselines by an average of 8.32%. Our implementations will be publicly available at: https://github.com/jianruichen/HNESP.
MeSH term(s)	Benchmarking ; Decision Making ; Entropy ; Learning ; Neural Networks, Computer
Language	English
Publishing date	2024-02-17
Publishing country	United States
Document type	Journal Article
ZDB-ID	740542-x
ISSN	1879-2782 ; 0893-6080
ISSN (online)	1879-2782
ISSN	0893-6080
DOI	10.1016/j.neunet.2024.106185
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Article: [Contrasting Responses of the Microbial Community Structure and Functional Traits to Soil pH in Purple Soils].

Wang, Zhi-Hui / Jiang, Xian-Jun

Huan jing ke xue= Huanjing kexue

2022 Volume 43, Issue 7, Page(s) 3876–3883

Abstract: To clarify the effect of pH on the structure and functional traits of soil microbial communities in purple soils, three purple upland soils developed from the same parent material that differed in pH were selected as the research objects, and metagenomic ...

Abstract	To clarify the effect of pH on the structure and functional traits of soil microbial communities in purple soils, three purple upland soils developed from the same parent material that differed in pH were selected as the research objects, and metagenomic shotgun sequencing was used to investigate the structure and functional traits of the microbial communities among different pH soils. The shotgun sequencing identified a total of 89 phyla, 222 classes, 527 orders, 1009 families, 2769 genera, and 14354 species in these soils. Regardless of the phylogenetic classification level, the microbial community structures of these three purple soils with different pHs were significantly different. RDA results corroborated the highly significant difference in the microbial community structures among the three purple soils with different pHs, and the soil properties tested here all had significant correlations with soil microbial community structure, in which the soil pH had the greatest effect (
MeSH term(s)	Humans ; Microbiota ; Nitrogen ; Phylogeny ; Soil/chemistry ; Soil Microbiology
Chemical Substances	Soil ; Nitrogen (N762921K75)
Language	Chinese
Publishing date	2022-06-01
Publishing country	China
Document type	Journal Article
ISSN	0250-3301
ISSN	0250-3301
DOI	10.13227/j.hjkx.202111055
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Article: Establishment and Validation of a Predictive Nomogram for Postoperative Survival of Stage I Non-Small Cell Lung Cancer.

Wang, Zhi-Hui / Deng, Lili

International journal of general medicine

2022 Volume 15, Page(s) 7287–7298

Abstract: Background: Surgical procedure is the preferred option for people with early-stage non-small cell lung cancer (NSCLC), while nearly 30% of patients experienced metastatic or recurrent tumor after operation. The primary intention of this context is to ... ...

Abstract	Background: Surgical procedure is the preferred option for people with early-stage non-small cell lung cancer (NSCLC), while nearly 30% of patients experienced metastatic or recurrent tumor after operation. The primary intention of this context is to summarize high-risk prognostic factors and set up a novel nomogram to predict the overall survival of individuals with stage I NSCLC after resection. Methods: Research objects, 10,218 patients with stage I NSCLC after operation from 2010 to 2015, were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors, confirmed by Cox regression analyses, were integrated into a nomogram, to predict the 3-and 5-year overall survival of these individuals. The model experienced internal validation of testing cohorts above and external validation crewed by 160 patients from China. Finally, the nomogram was evaluated through several verification methods such as concordance index (C-index), calibration plots and receiver operating characteristic curve (ROC). Results: Multivariate analysis identified that age, gender, histologic type, differentiation class, type of operation, T stage and treatment were significant predictive factors for the survival of stage I NSCLC. Based on these factors, a nomogram was constructed to predict the 3- and 5-year overall survival of these individuals. Meanwhile, in the training set, this nomogram displayed excellent superiority over the TNM staging system with abroad application, especially in C-index (0.669 vs 0.580) and the AUC (the Area Under ROC Curve) for the 3- and 5-year survival (0.678 vs 0.582; 0.650 vs 0.576). In the calibration curve, the curve representing predicted survival tended to align with the line representing actual survival as well. Conclusion: A nomogram was successfully created and verified to achieve the goal that made a rounded accurate prediction on the survival of postoperative I NSCLC patients in terms of the SEER database.
Language	English
Publishing date	2022-09-14
Publishing country	New Zealand
Document type	Journal Article
ZDB-ID	2452220-X
ISSN	1178-7074
ISSN	1178-7074
DOI	10.2147/IJGM.S361179
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Article ; Online: The application and research progress of bacteriophages in food safety.

Wang, Zhihui / Zhao, Xihong

Journal of applied microbiology

2022 Volume 133, Issue 4, Page(s) 2137–2147

Abstract: The abuse of antibiotics and the emergence of drug-resistant bacteria aggravate the problem of food safety. Finding safe and efficient antibiotic substitutes is an inevitable demand for ensuring the safety of animal-derived food. Bacteriophages are a ... ...

Abstract	The abuse of antibiotics and the emergence of drug-resistant bacteria aggravate the problem of food safety. Finding safe and efficient antibiotic substitutes is an inevitable demand for ensuring the safety of animal-derived food. Bacteriophages are a kind of virus that can infect bacteria, fungi or actinomycetes. They have advantages of simple structure, strong specificity and nontoxic side effects for the human body. Bacteriophages can not only differentiate live cells from dead ones but also detect bacteria in a viable but nonculturable state. These characteristics make bacteriophages more and more widely used in the food industry. This paper describes the concept and characteristics of bacteriophages, and introduces the application of bacteriophages in preharvest production, food processing, storage and sales. Several methods of using bacteriophages to detect foodborne pathogens are listed. Finally, the advantages and limitations of bacteriophages in the food industry are summarized, and the application prospect of bacteriophages in the food industry is discussed.
MeSH term(s)	Animals ; Anti-Bacterial Agents ; Bacteria ; Bacteriophages ; Food Handling ; Food Safety/methods ; Food-Processing Industry ; Humans
Chemical Substances	Anti-Bacterial Agents
Language	English
Publishing date	2022-04-11
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	1358023-1
ISSN	1365-2672 ; 1364-5072
ISSN (online)	1365-2672
ISSN	1364-5072
DOI	10.1111/jam.15555
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Article ; Online: Carbon emissions of power transmission and transformation projects in the whole life cycle for smart sustainable energy systems.

Wang, Zhihui / Hu, Long / Huang, Xiaojia / Tan, Jieren / Ye, Kaihui

Scientific reports

2024 Volume 14, Issue 1, Page(s) 3812

Abstract: The study investigates the optimization of life cycle carbon emissions in smart sustainable energy systems through power transformation and transmission project power load predictions. Firstly, a multi-task learning-based short-term user load forecasting ...

Abstract	The study investigates the optimization of life cycle carbon emissions in smart sustainable energy systems through power transformation and transmission project power load predictions. Firstly, a multi-task learning-based short-term user load forecasting technique is developed, where the power load curves of multiple residential customers are grouped and classified using the K-means clustering method. Additionally, the Bidirectional Long Short-Term Memory (BiLSTM) technique is introduced to anticipate the power load intelligently. Secondly, a life cycle carbon emission assessment model for the power transmission and transformation project (PTTP) is constructed based on the life cycle assessment (LCA) method, which divides the project's life cycle into four stages: production, installation and construction, operation and maintenance, and demolition. Finally, an experimental evaluation of this model is conducted. The results demonstrate that compared with the baseline model Long Short-Term Memory (LSTM), this model achieves a significantly lower average Mean Absolute Error (MAE) at 3.62% while achieving significantly higher accuracy in power load forecasting at 94.34%. A comprehensive examination of carbon emissions across all four phases reveals that overall carbon emissions are highest during the operation and maintenance stage followed by the equipment production stage and installation/construction stage, with the lowest overall carbon emissions observed. Hence, this study endeavors to forecast power load demand with precision and identify the principal determinants of carbon emissions in power engineering. By discerning and managing these key factors, an optimal, energy-efficient intelligent power load scheme can be derived.
Language	English
Publishing date	2024-02-15
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/s41598-024-54317-0
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Article: The application and research progress of bacteriophages in food safety

Wang, Zhihui / Zhao, Xihong

Journal of applied microbiology. 2022 Oct., v. 133, no. 4

2022

Abstract: The abuse of antibiotics and the emergence of drug‐resistant bacteria aggravate the problem of food safety. Finding safe and efficient antibiotic substitutes is an inevitable demand for ensuring the safety of animal‐derived food. Bacteriophages are a ... ...

Abstract	The abuse of antibiotics and the emergence of drug‐resistant bacteria aggravate the problem of food safety. Finding safe and efficient antibiotic substitutes is an inevitable demand for ensuring the safety of animal‐derived food. Bacteriophages are a kind of virus that can infect bacteria, fungi or actinomycetes. They have advantages of simple structure, strong specificity and nontoxic side effects for the human body. Bacteriophages can not only differentiate live cells from dead ones but also detect bacteria in a viable but nonculturable state. These characteristics make bacteriophages more and more widely used in the food industry. This paper describes the concept and characteristics of bacteriophages, and introduces the application of bacteriophages in preharvest production, food processing, storage and sales. Several methods of using bacteriophages to detect foodborne pathogens are listed. Finally, the advantages and limitations of bacteriophages in the food industry are summarized, and the application prospect of bacteriophages in the food industry is discussed.
Keywords	animal-based foods ; antibiotics ; drug resistance ; food industry ; food safety ; humans ; viruses
Language	English
Dates of publication	2022-10
Size	p. 2137-2147.
Publishing place	John Wiley & Sons, Ltd
Document type	Article
Note	REVIEW
ZDB-ID	1358023-1
ISSN	1365-2672 ; 1364-5072
ISSN (online)	1365-2672
ISSN	1364-5072
DOI	10.1111/jam.15555
Database	NAL-Catalogue (AGRICOLA)

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