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学者姓名：陈开志
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BackgroundThis study aimed to establish and validate machine learning (ML) models to predict the prognosis of regenerative endodontic procedures (REPs) clinically, assisting clinicians in decision-making and avoiding treatment failure.MethodsA total of 198 patients with 268 teeth were included for radiographic examination and measurement. Five Machine Learning (ML) models, including Random forest (RF), gradient boosting machine (GBM), extreme gradient boosting (XGB), Logistic regression (logR) and support vector machine (SVM) are implemented for the prediction on two datasets of follow-up periods of 1-year and 2-year, respectively. Using a stratified five folds of cross-validation method, each dataset is randomly divided into a training set and test set in a ratio of 8 : 2. Correlation analysis and importance ranking were performed for feature extraction. Seven performance metrics including area under curve (AUC), accuracy, F1-score, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated to compare the predictive performance.ResultsThe RF (Accuracy = 0.91, AUC = 0.94; Accuracy = 0.84, AUC = 0.86) and GBM (Accuracy = 0.91, AUC = 0.93; Accuracy = 0.84, AUC = 0.85) had the best and similar performance simultaneously in the prediction of 1-year follow-up period and 2-year follow-up period, respectively. The variables applied to predict the primary outcome in REPs were ranked accordingly to their values of feature importance, including age, sex, etiology, the number of root canals, trauma type, swelling or sinus tract, periapical lesion size, root development stage, pre-operative root resorption, medicaments, scaffold, second REPs, previous root canal filling.ConclusionsRF and GBM models outperformed XGB, logR, SVM models on the overall performance on our datasets, exhibiting the potential capability to predict the prognosis of REPs. The ranking of feature importance contributes to establishing the scoring system for prognosis prediction in REPs, assisting clinicians in decision-making.
Keyword ：
Machine learning Machine learning Prognosis prediction Prognosis prediction Regenerative endodontic procedures Regenerative endodontic procedures
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| GB/T 7714 | Lu, Jing , Cai, Qianqian , Chen, Kaizhi et al. Machine learning models for prognosis prediction in regenerative endodontic procedures [J]. | BMC ORAL HEALTH , 2025 , 25 (1) . |
| MLA | Lu, Jing et al. "Machine learning models for prognosis prediction in regenerative endodontic procedures" . | BMC ORAL HEALTH 25 . 1 (2025) . |
| APA | Lu, Jing , Cai, Qianqian , Chen, Kaizhi , Kahler, Bill , Yao, Jun , Zhang, Yanjun et al. Machine learning models for prognosis prediction in regenerative endodontic procedures . | BMC ORAL HEALTH , 2025 , 25 (1) . |
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Background Influenza outbreaks have occurred frequently these years, especially in the summer of 2022 when the number of influenza cases in southern provinces of China increased abnormally. However, the exact evidence of the driving factors involved in the prodrome period is unclear, posing great difficulties for early and accurate prediction in practical work. Methods In order to avoid the serious interference of strict prevention and control measures on the analysis of influenza influencing factors during the COVID-19 epidemic period, only the impact of meteorological and air quality factors on influenza A (H3N2) in Xiamen during the non coronavirus disease 2019 (COVID-19) period (2013/01/01-202/01/24) was analyzed using the distribution lag non-linear model. Phylogenetic analysis of influenza A (H3N2) during 2013-2022 was also performed. Influenza A (H3N2) was predicted through a random forest and long short-term memory (RF-LSTM) model via actual and forecasted meteorological and influenza A (H3N2) values. Results Twenty nine thousand four hundred thirty five influenza cases were reported in 2022, accounting for 58.54% of the total cases during 2013-2022. A (H3N2) dominated the 2022 summer epidemic season, accounting for 95.60%. The influenza cases in the summer of 2022 accounted for 83.72% of the year and 49.02% of all influenza reported from 2013 to 2022. Among them, the A (H3N2) cases in the summer of 2022 accounted for 83.90% of all A (H3N2) reported from 2013 to 2022. Daily precipitation(20-50 mm), relative humidity (70-78%), low (<= 3 h) and high (>= 7 h) sunshine duration, air temperature (<= 21 degrees C) and O-3 concentration (<= 30 mu g/m(3), > 85 mu g/m(3)) had significant cumulative effects on influenza A (H3N2) during the non-COVID-19 period. The daily values of PRE, RHU, SSD, and TEM in the prodrome period of the abnormal influenza A (H3N2) epidemic (19-22 weeks) in the summer of 2022 were significantly different from the average values of the same period from 2013 to 2019 (P < 0.05). The minimum RHU value was 70.5%, the lowest TEM value was 16.0 degrees C, and there was no sunlight exposure for 9 consecutive days. The highest O-3 concentration reached 164 g/m(3). The range of these factors were consistent with the risk factor range of A (H3N2). The common influenza A (H3N2) variant genotype in 2022 was 3 C.2a1b.2a.1a. It was more accurate to predict influenza A (H3N2) with meteorological forecast values than with actual values only. Conclusion The extreme weather conditions of sustained low temperature and wet rain may have been important driving factors for the abnormal influenza A (H3N2) epidemic. A low vaccination rate, new mutated strains, and insufficient immune barriers formed by natural infections may have exacerbated this epidemic. Meteorological forecast values can aid in the early prediction of influenza outbreaks. This study can help relevant departments prepare for influenza outbreaks during extreme weather, provide a scientific basis for prevention strategies and risk warnings, better adapt to climate change, and improve public health.
Keyword ：
Air quality Air quality Influenza Influenza LSTM LSTM Meteorological factors Meteorological factors Phylogenetic analysis Phylogenetic analysis Random forest (RF) Random forest (RF)
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| GB/T 7714 | Zhu, Hansong , Qi, Feifei , Wang, Xiaoying et al. Study of the driving factors of the abnormal influenza A (H3N2) epidemic in 2022 and early predictions in Xiamen, China [J]. | BMC INFECTIOUS DISEASES , 2024 , 24 (1) . |
| MLA | Zhu, Hansong et al. "Study of the driving factors of the abnormal influenza A (H3N2) epidemic in 2022 and early predictions in Xiamen, China" . | BMC INFECTIOUS DISEASES 24 . 1 (2024) . |
| APA | Zhu, Hansong , Qi, Feifei , Wang, Xiaoying , Zhang, Yanhua , Chen, Fangjingwei , Cai, Zhikun et al. Study of the driving factors of the abnormal influenza A (H3N2) epidemic in 2022 and early predictions in Xiamen, China . | BMC INFECTIOUS DISEASES , 2024 , 24 (1) . |
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| GB/T 7714 | Zhu, Hansong , Qi, Feifei , Wang, Xiaoying et al. Study of the driving factors of the abnormal influenza A (H3N2) epidemic in 2022 and early predictions in Xiamen, China (vol 24, 1093, 2024) [J]. | BMC INFECTIOUS DISEASES , 2024 , 24 (1) . |
| MLA | Zhu, Hansong et al. "Study of the driving factors of the abnormal influenza A (H3N2) epidemic in 2022 and early predictions in Xiamen, China (vol 24, 1093, 2024)" . | BMC INFECTIOUS DISEASES 24 . 1 (2024) . |
| APA | Zhu, Hansong , Qi, Feifei , Wang, Xiaoying , Zhang, Yanhua , Chen, Fangjingwei , Cai, Zhikun et al. Study of the driving factors of the abnormal influenza A (H3N2) epidemic in 2022 and early predictions in Xiamen, China (vol 24, 1093, 2024) . | BMC INFECTIOUS DISEASES , 2024 , 24 (1) . |
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Water quality prediction is of great significance in pollution control, prevention, and management. Deep learning models have been applied to water quality prediction in many recent studies. However, most existing deep learning models for water quality prediction are used for single-site data, only considering the time dependency of water quality data and ignoring the spatial correlation among multi-sites. This research defines and analyzes the non-aligned spatial correlations that exist in multi-site water quality data. Then deploy spatial-temporal graph convolution to process water quality data, which takes into account both the temporal and spatial correlation of multi-site water quality data. A multi-site water pollution prediction method called W-WaveNet is proposed that integrates adaptive graph convolution and Convolutional Neural Network, Long Short-Term Memory (CNN-LSTM). It integrates temporal and spatial models by interleaved stacking. Theoretical analysis shows that the method can deal with non-aligned spatial correlations in different time spans, which is suitable for water quality data processing. The model validates water quality data generated on two real river sections that have multiple sites. The experimental results were compared with the results of Support Vector Regression, CNN-LSTM, and Spatial-Temporal Graph Convolutional Networks (STGCN). It shows that when W-WaveNet predicts water quality over two river sections, the average Mean Absolute Error is 0.264, which is 45.2% lower than the commonly used CNN-LSTM model and 23.8% lower than the STGCN. The comparison experiments also demonstrate that W-WaveNet has a more stable performance in predicting longer sequences.
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| GB/T 7714 | Yang, Shaojun , Zhong, Shangping , Chen, Kaizhi . W-WaveNet: A multi-site water quality prediction model incorporating adaptive graph convolution and CNN-LSTM [J]. | PLOS ONE , 2024 , 19 (3) . |
| MLA | Yang, Shaojun et al. "W-WaveNet: A multi-site water quality prediction model incorporating adaptive graph convolution and CNN-LSTM" . | PLOS ONE 19 . 3 (2024) . |
| APA | Yang, Shaojun , Zhong, Shangping , Chen, Kaizhi . W-WaveNet: A multi-site water quality prediction model incorporating adaptive graph convolution and CNN-LSTM . | PLOS ONE , 2024 , 19 (3) . |
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Objective At different times, public health faces various challenges and the degree of intervention measures varies. The research on the impact and prediction of meteorology factors on influenza is increasing gradually, however, there is currently no evidence on whether its research results are affected by different periods. This study aims to provide limited evidence to reveal this issue. Methods Daily data on influencing factors and influenza in Xiamen were divided into three parts: overall period (phase AB), non-COVID-19 epidemic period (phase A), and COVID-19 epidemic period (phase B). The association between influencing factors and influenza was analysed using generalized additive models (GAMs). The excess risk (ER) was used to represent the percentage change in influenza as the interquartile interval (IQR) of meteorology factors increases. The 7-day average daily influenza cases were predicted using the combination of bi-directional long short memory (Bi-LSTM) and random forest (RF) through multi-step rolling input of the daily multifactor values of the previous 7-day. Results In periods A and AB, air temperature below 22 degrees C was a risk factor for influenza. However, in phase B, temperature showed a U-shaped effect on it. Relative humidity had a more significant cumulative effect on influenza in phase AB than in phase A (peak: accumulate 14d, AB: ER = 281.54, 95% CI = 245.47 similar to 321.37; A: ER = 120.48, 95% CI = 100.37 similar to 142.60). Compared to other age groups, children aged 4-12 were more affected by pressure, precipitation, sunshine, and day light, while those aged >= 13 were more affected by the accumulation of humidity over multiple days. The accuracy of predicting influenza was highest in phase A and lowest in phase B. Conclusions The varying degrees of intervention measures adopted during different phases led to significant differences in the impact of meteorology factors on influenza and in the influenza prediction. In association studies of respiratory infectious diseases, especially influenza, and environmental factors, it is advisable to exclude periods with more external interventions to reduce interference with environmental factors and influenza related research, or to refine the model to accommodate the alterations brought about by intervention measures. In addition, the RF-Bi-LSTM model has good predictive performance for influenza.
Keyword ：
Bi-LSTM Bi-LSTM COVID-19 COVID-19 Influenza Influenza Meteorological Meteorological Random forest (RF) Random forest (RF)
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| GB/T 7714 | Zhu, Hansong , Chen, Si , Qin, Weixia et al. Study on the impact of meteorological factors on influenza in different periods and prediction based on artificial intelligence RF-Bi-LSTM algorithm: to compare the COVID-19 period with the non-COVID-19 period [J]. | BMC INFECTIOUS DISEASES , 2024 , 24 (1) . |
| MLA | Zhu, Hansong et al. "Study on the impact of meteorological factors on influenza in different periods and prediction based on artificial intelligence RF-Bi-LSTM algorithm: to compare the COVID-19 period with the non-COVID-19 period" . | BMC INFECTIOUS DISEASES 24 . 1 (2024) . |
| APA | Zhu, Hansong , Chen, Si , Qin, Weixia , Aynur, Joldosh , Chen, Yuyan , Wang, Xiaoying et al. Study on the impact of meteorological factors on influenza in different periods and prediction based on artificial intelligence RF-Bi-LSTM algorithm: to compare the COVID-19 period with the non-COVID-19 period . | BMC INFECTIOUS DISEASES , 2024 , 24 (1) . |
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Currently, fabric image retrieval faces challenges such as the high cost of image annotation and its vulnerability to adversarial perturbations. To minimize manual supervision and enhance the robustness of the retrieval system, this study proposes a robust deep image retrieval algorithm using multi-view self-supervised product quantization for artificially generated fabric images. The method introduces a multi-view module, which includes two views enhanced by AutoAugment, an adversarial view and a high-frequency view of the unlabeled images. AutoAugment can generate more varied data variations, which allows the model to learn more about the different features and structures of the fabric texture; fabric images are usually of high complexity and diversity, and adding the adversarial sample into the model training can add more noise and variations, which is one of the best existing ways to defend against adversarial attacks; the high-frequency component can make the edges, details, and contrasts in the fabric image clearer. A robust cross quantized contrastive loss function is also designed to jointly learn codewords and deep visual descriptors by comparing multiple views, effectively increasing the model’s robustness and generalization. The method's effectiveness is demonstrated by experimental results on multiple datasets, which can significantly improve the robustness of the retrieval system compared to other state-of-the-art retrieval algorithms. Our method presents a new approach for fabric image retrieval and has great significance for improving its performance. © 2024 SPIE. All rights reserved.
Keyword ：
Image compression Image compression Image enhancement Image enhancement Image retrieval Image retrieval Search engines Search engines Textures Textures
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| GB/T 7714 | Zhuo, Yudan , Zhong, Shangping , Chen, Kaizhi . Multi-view Self-supervised Product Quantization for Robust Fabric Image Retrieval [C] . 2024 . |
| MLA | Zhuo, Yudan et al. "Multi-view Self-supervised Product Quantization for Robust Fabric Image Retrieval" . (2024) . |
| APA | Zhuo, Yudan , Zhong, Shangping , Chen, Kaizhi . Multi-view Self-supervised Product Quantization for Robust Fabric Image Retrieval . (2024) . |
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Lace texture, as a manually designed texture image, needs to possess a series of essential aesthetic characteristics, such as periodicity, symmetry, and blank-leaving in artistic design creation. It requires human designers to spend a lot of time and effort, so it is necessary to apply generative models to generate lace images. In image generation tasks, compared to models such as DCGAN, CycleGAN, and ProGAN, although images generated using StyleGAN2 perform well in terms of resolution, texture details, and periodicity, they still perform poorly in terms of symmetry in lace images. To address the above issues, this article proposes an improved model SStyleGAN (Symmetry StyleGAN) based on StyleGAN2. In terms of discriminators, in order to enhance the attention of the proposed model to image symmetry, we have added a symmetry discriminator, that is, SStyleGAN adopts a dual discriminator structure; In terms of generator, in order to improve the similarity of the feature maps on the left and right sides of the lace image, this paper adds a mean square error loss term based on the loss function of StyleGAN2; In terms of noise input, in order to control the symmetry of the lace image at details such as lace edges, the noise of the StyleGAN2 model is modified to a symmetrical structure, so that the noise input itself has symmetry. In addition to the commonly used FID (Fréchet Insertion Distance) in the generative model, we also used the SSIM (Structural Similarity) metric for the evaluation of the experimental results in this article to detect the symmetry of the generated images. The experimental results show that compared to the lace images generated by the StyleGAN2 model, the lace images generated by the model proposed in this paper not only inherit the advantages of the former, but also have symmetry characteristics. © 2024 SPIE.
Keyword ：
Discriminators Discriminators Generative adversarial networks Generative adversarial networks Image enhancement Image enhancement Image texture Image texture Mean square error Mean square error Textures Textures
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| GB/T 7714 | Li, Jian , Zhong, Shangping , Chen, Kaizhi . SStyleGAN: a StyleGAN model for generating symmetrical lace images [C] . 2024 . |
| MLA | Li, Jian et al. "SStyleGAN: a StyleGAN model for generating symmetrical lace images" . (2024) . |
| APA | Li, Jian , Zhong, Shangping , Chen, Kaizhi . SStyleGAN: a StyleGAN model for generating symmetrical lace images . (2024) . |
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To cope with the threat of image content tampering in real scenes, this paper develops a multi-view spatial-channel attention network (MSCA-Net), which can use multi-view features and multi-scale features to detect whether an image has been tampered with and predict tampered regions. By introducing the frequency domain view of the image, the model can use the noise distribution around the tampered region to learn semantically independent features and detect subtle tampering traces that are difficult to detect in the RGB domain. Secondly, a new Efficient Spatial-Channel Attention Module (ESCM) is proposed to capture the correlation between different channels and between global pixels. MSCA-Net improves the localization performance of tampered regions on real-scene images by generating segmentation masks step by step at multiple scales through a progressive guidance mechanism. MSCA-Net runs very fast and is capable of processing 1080P resolution images at 40FPS+. Extensive experimental results demonstrate the promising performance of MSCA-Net on both image-level and pixel-level tampering detection tasks. © 2024 SPIE. All rights reserved.
Keyword ：
Behavioral research Behavioral research Feature extraction Feature extraction Frequency domain analysis Frequency domain analysis Image enhancement Image enhancement Image segmentation Image segmentation Pixels Pixels
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| GB/T 7714 | Liu, Hanquan , Zhong, Shangping , Chen, Kaizhi . Multi-View Image Tampering Detection and Localization in Real Scene Based on Spatial-Channel Attention [C] . 2024 . |
| MLA | Liu, Hanquan et al. "Multi-View Image Tampering Detection and Localization in Real Scene Based on Spatial-Channel Attention" . (2024) . |
| APA | Liu, Hanquan , Zhong, Shangping , Chen, Kaizhi . Multi-View Image Tampering Detection and Localization in Real Scene Based on Spatial-Channel Attention . (2024) . |
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共情回复生成旨在理解对话中用户的经历与感受并表达出合理的回复.心理学理论认为,角色是人格的外在表现,与共情密切相关.然而,现有工作主要关注共情的认知因素和情绪因素,忽略有益于共情的角色因素,导致缺少个性化的共情回复.为了解决该问题,文中提出角色增强的共情回复生成模型(Persona-Enhanced Empathetic Response Generation Model,PERG).首先,为了有效利用角色信息,提出角色增强编码模块,通过编码器捕获上下文、情境及角色信息的深层语义关系,结合上下文和情境筛选角色信息,提升模型对说话者与回应者角色的理解,增强共情能力.然后,在角色调控解码模块中,设计基于多解码器融合的调控机制,有效结合角色信息,调节上下文和情境对共情回复的影响,生成高度个性化的共情回复.在公开的共情回复EmpatheticDialogues数据集上的实验表明,PERG在多个指标上均取得较优值.
Keyword ：
共情回复 共情回复 对话系统 对话系统 自然语言处理 自然语言处理 角色增强 角色增强 角色调控 角色调控
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| GB/T 7714 | 吴运兵 , 叶成龙 , 阴爱英 et al. 角色增强的共情回复生成 [J]. | 模式识别与人工智能 , 2024 , 37 (12) : 1043-1055 . |
| MLA | 吴运兵 et al. "角色增强的共情回复生成" . | 模式识别与人工智能 37 . 12 (2024) : 1043-1055 . |
| APA | 吴运兵 , 叶成龙 , 阴爱英 , 陈开志 , 杨州 . 角色增强的共情回复生成 . | 模式识别与人工智能 , 2024 , 37 (12) , 1043-1055 . |
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BackgroundThis study adopted complete meteorological indicators, including eight items, to explore their impact on hand, foot, and mouth disease (HFMD) in Fuzhou, and predict the incidence of HFMD through the long short-term memory (LSTM) neural network algorithm of artificial intelligence.MethodA distributed lag nonlinear model (DLNM) was used to analyse the influence of meteorological factors on HFMD in Fuzhou from 2010 to 2021. Then, the numbers of HFMD cases in 2019, 2020 and 2021 were predicted using the LSTM model through multifactor single-step and multistep rolling methods. The root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to evaluate the accuracy of the model predictions.ResultsOverall, the effect of daily precipitation on HFMD was not significant. Low (4 hPa) and high (>= 21 hPa) daily air pressure difference (PRSD) and low (< 7 degrees C) and high (> 12 degrees C) daily air temperature difference (TEMD) were risk factors for HFMD. The RMSE, MAE, MAPE and SMAPE of using the weekly multifactor data to predict the cases of HFMD on the following day, from 2019 to 2021, were lower than those of using the daily multifactor data to predict the cases of HFMD on the following day. In particular, the RMSE, MAE, MAPE and SMAPE of using weekly multifactor data to predict the following week's daily average cases of HFMD were much lower, and similar results were also found in urban and rural areas, which indicating that this approach was more accurate.ConclusionThis study's LSTM models combined with meteorological factors (excluding PRE) can be used to accurately predict HFMD in Fuzhou, especially the method of predicting the daily average cases of HFMD in the following week using weekly multifactor data.
Keyword ：
Air temperature Air temperature DLNM DLNM HFMD HFMD LSTM LSTM Meteorological Meteorological Relative humidity Relative humidity
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| GB/T 7714 | Zhu, Hansong , Chen, Si , Liang, Rui et al. Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China [J]. | BMC INFECTIOUS DISEASES , 2023 , 23 (1) . |
| MLA | Zhu, Hansong et al. "Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China" . | BMC INFECTIOUS DISEASES 23 . 1 (2023) . |
| APA | Zhu, Hansong , Chen, Si , Liang, Rui , Feng, Yulin , Joldosh, Aynur , Xie, Zhonghang et al. Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China . | BMC INFECTIOUS DISEASES , 2023 , 23 (1) . |
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