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  1. Article ; Online: Rice quality prediction and assessment of pesticide residue changes during storage based on Quatformer.

    Jiang, Tongqiang / Deng, Furong / Dong, Wei / Zhang, Qingchuan / Liu, Peng

    Scientific reports

    2024  Volume 14, Issue 1, Page(s) 9130

    Abstract: Rice serves as a fundamental food staple for humans. Its production process, however, unavoidably exposes it to pesticides which may detrimentally impact its quality due to residues. Therefore, it is extremely necessary to monitor pesticide residues on ... ...

    Abstract Rice serves as a fundamental food staple for humans. Its production process, however, unavoidably exposes it to pesticides which may detrimentally impact its quality due to residues. Therefore, it is extremely necessary to monitor pesticide residues on rice during storage. In this research, the Quatformer model, which considers the effects of temperature and humidity on pesticide residues in rice grains, was utilized to forecast the amount of pesticide residues in rice grains during the storage process, and the predicted results were combined with actual observations to form a quality assessment index. By applying the K-Means algorithm, the quality of rice grains was graded and assessed. The findings indicated that the model had high prediction accuracy, and the MAE, MSE, MAPE, RMSE and SMAPE indexes were calculated to be 0.0112, 0.0814, 0.1057, 0.1055 and 0.0204, respectively. These findings provide valuable technical and theoretical support for planning storage conditions, enhancing pesticide residue decomposition, and monitoring rice quality during storage.
    MeSH term(s) Oryza/chemistry ; Pesticide Residues/analysis ; Food Storage/methods ; Food Contamination/analysis ; Temperature ; Algorithms ; Humidity
    Chemical Substances Pesticide Residues
    Language English
    Publishing date 2024-04-21
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-024-59816-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Innovative synthesis technique for high-performance dielectric resonator antennas: laser-induced shockwave sintering of potassium sodium niobate (KNN).

    Zhang, Hao / Joo, Yun Hwan / Wang, Yue / Yi, Tongqiang / Sung, Tae Hyun

    Nanotechnology

    2024  Volume 35, Issue 27

    Abstract: This study explored the synthesis and sintering of potassium sodium niobate (KNN) nanoparticles, emphasizing morphology, crystal structure, and sintering methods. The as-synthesized KNN nanoparticles exhibited a spherical morphology below 200 nm. Solid ... ...

    Abstract This study explored the synthesis and sintering of potassium sodium niobate (KNN) nanoparticles, emphasizing morphology, crystal structure, and sintering methods. The as-synthesized KNN nanoparticles exhibited a spherical morphology below 200 nm. Solid state sintering (SSS) and laser-induced shockwave sintering (LISWS) were compared, with LISWS producing denser microstructures and improved grain growth. Raman spectroscopy and x-ray diffraction confirmed KNN perovskite structure, with LISWS demonstrating higher purity. High-resolution x-ray photoelectron spectroscopy spectra indicated increased binding energies in LISWS, reflecting enhanced density and crystallinity. Dielectric and loss tangent analyses showed temperature-dependent behavior, with LISWS-3 exhibiting superior properties. Antenna performance assessments revealed LISWS-3's improved directivity and reduced sidelobe radiation compared to SSS, attributed to its denser microstructure. Overall, LISWS proved advantageous for enhancing KNN ceramics, particularly in antenna applications.
    Language English
    Publishing date 2024-04-15
    Publishing country England
    Document type Journal Article
    ZDB-ID 1362365-5
    ISSN 1361-6528 ; 0957-4484
    ISSN (online) 1361-6528
    ISSN 0957-4484
    DOI 10.1088/1361-6528/ad373a
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Predicting the cognitive function status in end-stage renal disease patients at a functional subnetwork scale.

    Lu, Yu / Liu, Tongqiang / Sheng, Quan / Zhang, Yutao / Shi, Haifeng / Jiao, Zhuqing

    Mathematical biosciences and engineering : MBE

    2024  Volume 21, Issue 3, Page(s) 3838–3859

    Abstract: Brain functional networks derived from functional magnetic resonance imaging (fMRI) provide a promising approach to understanding cognitive processes and predicting cognitive abilities. The topological attribute parameters of global networks are taken as ...

    Abstract Brain functional networks derived from functional magnetic resonance imaging (fMRI) provide a promising approach to understanding cognitive processes and predicting cognitive abilities. The topological attribute parameters of global networks are taken as the features from the overall perspective. It is constrained to comprehend the subtleties and variances of brain functional networks, which fell short of thoroughly examining the complex relationships and information transfer mechanisms among various regions. To address this issue, we proposed a framework to predict the cognitive function status in the patients with end-stage renal disease (ESRD) at a functional subnetwork scale (CFSFSS). The nodes from different network indicators were combined to form the functional subnetworks. The area under the curve (AUC) of the topological attribute parameters of functional subnetworks were extracted as features, which were selected by the minimal Redundancy Maximum Relevance (mRMR). The parameter combination with improved fitness was searched by the enhanced whale optimization algorithm (E-WOA), so as to optimize the parameters of support vector regression (SVR) and solve the global optimization problem of the predictive model. Experimental results indicated that CFSFSS achieved superior predictive performance compared to other methods, by which the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were up to 0.5951, 0.0281 and 0.9994, respectively. The functional subnetwork effectively identified the active brain regions associated with the cognitive function status, which offered more precise features. It not only helps to more accurately predict the cognitive function status, but also provides more references for clinical decision-making and intervention of cognitive impairment in ESRD patients.
    MeSH term(s) Animals ; Humans ; Cognition ; Brain/diagnostic imaging ; Magnetic Resonance Imaging/methods ; Algorithms ; Whales ; Kidney Failure, Chronic/diagnostic imaging
    Language English
    Publishing date 2024-03-28
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2265126-3
    ISSN 1551-0018 ; 1551-0018
    ISSN (online) 1551-0018
    ISSN 1551-0018
    DOI 10.3934/mbe.2024171
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Analysis of factors affecting crash under risk scenarios based on driver homogenous clustering.

    Lili Zheng / Yanlin Li / Tongqiang Ding / Fanyun Meng / Yanlin Zhang

    PLoS ONE, Vol 18, Iss 10, p e

    2023  Volume 0293307

    Abstract: Research on road safety has focused on analyzing the factors that affect crashes. However, previous studies have often neglected differences in crash causation among heterogeneous clusters of drivers. In particular, the differences in the combined effect ...

    Abstract Research on road safety has focused on analyzing the factors that affect crashes. However, previous studies have often neglected differences in crash causation among heterogeneous clusters of drivers. In particular, the differences in the combined effect mechanisms of the factors in the risk scenarios have not been completely explained. Therefore, this study used the K-means algorithm to perform multidimensional feature homogeneous clustering for drivers involved in crashes and near-crashes. Structural equation modeling involving mediating effects was introduced to explore the direct and indirect effects of each influencing factor on vehicle crashes under risk scenarios and compare the differences in crash causation among different driver clusters. The results indicate that the drivers who experienced the risk scenarios can be classified into two homogeneous driver clusters. Significant differences exist in the demographic characteristics, intrinsic driving characteristics, and crash rates between them. In the risk scenario, traffic factors, distraction state, crash avoidance reaction, and maneuver judgment directly affect the crash outcomes of the two cluster drivers. Demographic characteristics and environmental factors have fewer direct influence on the crash outcomes of two-cluster drivers, but produce more complex mediating effects. Analysis of the differences in the influence of factors between clusters indicates that the fundamental cause of crashes for cluster 1 drivers includes poor driving skills. In contrast, cluster 2 drivers' crashes were more influenced by traffic conditions and their safety awareness. The analysis method of this study can be used to develop more targeted road safety policies to reduce the occurrence of vehicle crashes.
    Keywords Medicine ; R ; Science ; Q
    Subject code 380
    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)

    Full text online

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  5. Article ; Online: Accident Probability Prediction and Analysis of Bus Drivers Based on Occupational Characteristics

    Tongqiang Ding / Lei Yuan / Zhiqiang Li / Jianfeng Xi / Kexin Zhang

    Applied Sciences, Vol 14, Iss 1, p

    2023  Volume 279

    Abstract: A city bus carries a large number of passengers, and any traffic accidents can lead to severe casualties and property losses. Hence, predicting the likelihood of accidents among bus drivers is paramount. This paper considered occupational driving ... ...

    Abstract A city bus carries a large number of passengers, and any traffic accidents can lead to severe casualties and property losses. Hence, predicting the likelihood of accidents among bus drivers is paramount. This paper considered occupational driving characteristics such as cumulative driving duration, station entry and exit features, and peak driving times, and categorical boosting (CatBoost) was used to construct an accident probability prediction model. Its effectiveness was confirmed by the daily management data of a Chongqing bus company in June. For data processing, Multiple Imputation by Chained Equations for Random Forests (MICEForest) was used for data filling. In terms of prediction, a comparative analysis of four boosted trees revealed that CatBoost exhibited superior performance. To analyze the critical factors affecting the probability of bus driver accidents, SHapley Additive exPlanations (SHAP) was applied to visualize and interpret the results. In addition to the significant effects of age, rainfall, and azimuthal change, etc., we innovatively discovered that the proportion of driving duration during peak duration, the dispersion when entering and exiting stations, the proportion of driving duration within a week, and the accumulated driving duration of the previous week also had varying degrees of impact on accident probability. Our research and findings provide a new idea of accident prediction for professional drivers and direct theoretical support for the accident risk management of bus drivers.
    Keywords bus drivers ; accident probability prediction ; CatBoost ; driving duration ; entry and exit bus stop ; SHAP ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
    Subject code 380
    Language English
    Publishing date 2023-12-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  6. Article: Prediction of Safety Risk Levels of Benzopyrene Residues in Edible Oils in China Based on the Variable-Weight Combined LSTM-XGBoost Prediction Model.

    Hao, Cheng / Zhang, Qingchuan / Wang, Shimin / Jiang, Tongqiang / Dong, Wei

    Foods (Basel, Switzerland)

    2023  Volume 12, Issue 11

    Abstract: To assess and predict the food safety risk of benzopyrene (BaP) in edible oils in China, this study collected national sampling data of edible oils from 20 Chinese provinces and their prefectures in 2019, and constructed a risk assessment model of BaP in ...

    Abstract To assess and predict the food safety risk of benzopyrene (BaP) in edible oils in China, this study collected national sampling data of edible oils from 20 Chinese provinces and their prefectures in 2019, and constructed a risk assessment model of BaP in edible oils with consumption data. Initially, the k-means algorithm was used for risk classification; then the data were pre-processed and trained to predict the data using the Long Short-Term Memory (LSTM) and the eXtreme Gradient Boosting (XGBoost) models, respectively, and finally, the two models were combined using the inverse error method. To test the effectiveness of the prediction model, this study experimentally validated the model according to five evaluation metrics: root mean square error (RMSE), mean absolute error (MAE), precision, recall, and F1 score. The variable-weight combined LSTM-XGBoost prediction model proposed in this paper achieved a precision of 94.62%, and the F1 score value reached 95.16%, which is significantly better than other neural network models; the results demonstrate that the prediction model has certain stability and feasibility. Overall, the combined model used in this study not only improves the accuracy but also enhances the practicality, real-time capabilities, and expandability of the model.
    Language English
    Publishing date 2023-06-01
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2704223-6
    ISSN 2304-8158
    ISSN 2304-8158
    DOI 10.3390/foods12112241
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Analysis of factors affecting crash under risk scenarios based on driver homogenous clustering.

    Zheng, Lili / Li, Yanlin / Ding, Tongqiang / Meng, Fanyun / Zhang, Yanlin

    PloS one

    2023  Volume 18, Issue 10, Page(s) e0293307

    Abstract: Research on road safety has focused on analyzing the factors that affect crashes. However, previous studies have often neglected differences in crash causation among heterogeneous clusters of drivers. In particular, the differences in the combined effect ...

    Abstract Research on road safety has focused on analyzing the factors that affect crashes. However, previous studies have often neglected differences in crash causation among heterogeneous clusters of drivers. In particular, the differences in the combined effect mechanisms of the factors in the risk scenarios have not been completely explained. Therefore, this study used the K-means algorithm to perform multidimensional feature homogeneous clustering for drivers involved in crashes and near-crashes. Structural equation modeling involving mediating effects was introduced to explore the direct and indirect effects of each influencing factor on vehicle crashes under risk scenarios and compare the differences in crash causation among different driver clusters. The results indicate that the drivers who experienced the risk scenarios can be classified into two homogeneous driver clusters. Significant differences exist in the demographic characteristics, intrinsic driving characteristics, and crash rates between them. In the risk scenario, traffic factors, distraction state, crash avoidance reaction, and maneuver judgment directly affect the crash outcomes of the two cluster drivers. Demographic characteristics and environmental factors have fewer direct influence on the crash outcomes of two-cluster drivers, but produce more complex mediating effects. Analysis of the differences in the influence of factors between clusters indicates that the fundamental cause of crashes for cluster 1 drivers includes poor driving skills. In contrast, cluster 2 drivers' crashes were more influenced by traffic conditions and their safety awareness. The analysis method of this study can be used to develop more targeted road safety policies to reduce the occurrence of vehicle crashes.
    MeSH term(s) Automobile Driving ; Accidents, Traffic/prevention & control ; Cluster Analysis ; Latent Class Analysis ; Algorithms ; Risk Factors
    Language English
    Publishing date 2023-10-20
    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.0293307
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article: Development and Validation of a Novel Gene Signature for Predicting the Prognosis of Idiopathic Pulmonary Fibrosis Based on Three Epithelial-Mesenchymal Transition and Immune-Related Genes.

    Zheng, Jiafeng / Dong, Hanquan / Zhang, Tongqiang / Ning, Jing / Xu, Yongsheng / Cai, Chunquan

    Frontiers in genetics

    2022  Volume 13, Page(s) 865052

    Abstract: Background: ...

    Abstract Background:
    Language English
    Publishing date 2022-04-26
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2606823-0
    ISSN 1664-8021
    ISSN 1664-8021
    DOI 10.3389/fgene.2022.865052
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article: Informer-Based Safety Risk Prediction of Heavy Metals in Rice in China.

    Lu, Ping / Dong, Wei / Jiang, Tongqiang / Liu, Tianqi / Hu, Tianyu / Zhang, Qingchuan

    Foods (Basel, Switzerland)

    2023  Volume 12, Issue 3

    Abstract: Focused supervision and early warning of heavy metal (HM)-contaminated rice areas can effectively protect people's livelihood security and maintain social stability. To improve the accuracy of risk prediction, an Informer-based safety risk prediction ... ...

    Abstract Focused supervision and early warning of heavy metal (HM)-contaminated rice areas can effectively protect people's livelihood security and maintain social stability. To improve the accuracy of risk prediction, an Informer-based safety risk prediction model for HMs in rice is constructed in this paper. First, based on the national sampling data and residential consumption statistics of rice, we construct a dataset of evaluation indicators that can characterize the level of rice safety risk so as to form a safety risk space. Second, based on the K-medoids clustering algorithm, we classify the rice safety risk space into levels. Finally, we use the Informer neural network model to predict the safety risk indicators of rice in each province so as to predict the safety risk level. This study compares the prediction accuracy of a self-constructed dataset of rice safety risk assessment indicators. The experimental results show that the prediction precision of the method proposed in this paper reaches 99.17%, 91.77%, and 91.33% for low, medium, and high risk levels, respectively. The model provides technical support and a scientific basis for screening the time and area of HM contamination of rice, which needs focus.
    Language English
    Publishing date 2023-01-26
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2704223-6
    ISSN 2304-8158
    ISSN 2304-8158
    DOI 10.3390/foods12030542
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: Case Report: Clinical Analysis of Fulminant

    Zhang, Tongqiang / Han, Chunjiao / Guo, Wei / Ning, Jing / Cai, Chunquan / Xu, Yongsheng

    Frontiers in pediatrics

    2021  Volume 9, Page(s) 741663

    Abstract: ... ...

    Abstract Fulminant
    Language English
    Publishing date 2021-12-09
    Publishing country Switzerland
    Document type Case Reports
    ZDB-ID 2711999-3
    ISSN 2296-2360
    ISSN 2296-2360
    DOI 10.3389/fped.2021.741663
    Database MEDical Literature Analysis and Retrieval System OnLINE

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