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Article ; Online: Continuous Use Intention of Mobile Social Network Information Service Based on User Behavior Perception

Jingjing Lv / Nan Wang / Yin Ye / Wenhe Li

Computational Intelligence and Neuroscience, Vol

2022 Volume 2022

Abstract: In order to improve the willingness of continuous use of mobile social network information services, this study combines user behavior perception to analyze the continuous use of mobile social network information services and proposes a data coverage ... ...

Abstract	In order to improve the willingness of continuous use of mobile social network information services, this study combines user behavior perception to analyze the continuous use of mobile social network information services and proposes a data coverage optimization strategy based on service quality perception. Furthermore, this study measures participants’ regional preferences based on the duration of participants in the perceptual region and the number of historical perceptual tasks completed on the perceptual region. In addition, this study designs a perceptual data coverage optimization algorithm to optimize the perceptual data coverage and ensure the real-time validity of the perceptual data. Through algorithm research and systematic evaluation, it can be seen that the continuous use willingness system of mobile social network information service based on user behavior perception can basically meet the actual needs.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7 ; Neurosciences. Biological psychiatry. Neuropsychiatry ; RC321-571
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Hindawi Limited
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Immunogenicity and safety of heterologous versus homologous prime-boost schedules with an adenoviral vectored and mRNA COVID-19 vaccine

Jingjing Lv / Hui Wu / Junjie Xu / Jiaye Liu

Infectious Diseases of Poverty, Vol 11, Iss 1, Pp 1-

a systematic review

2022 Volume 17

Abstract: Abstract Background Heterologous prime-boost with ChAdOx1 nCoV-19 vector vaccine (ChAd) and a messenger RNA vaccine (BNT or mRNA-1273) has been widely facilitating mass coronavirus disease 2019 (COVID-19) immunisation. This review aimed to synthesize ... ...

Abstract	Abstract Background Heterologous prime-boost with ChAdOx1 nCoV-19 vector vaccine (ChAd) and a messenger RNA vaccine (BNT or mRNA-1273) has been widely facilitating mass coronavirus disease 2019 (COVID-19) immunisation. This review aimed to synthesize immunogenicity and reactogenicity of heterologous immunisations with ChAd and BNT (mRNA-1273) vaccine compared with homologous ChAd or BNT (mRNA-1273) immunisation. Methods PubMed, Web of Science, and Embase databases were searched from inception to March 7, 2022. Immunogenicity involving serum antibodies against different SAS-CoV-2 fragments, neutralizing antibody, or spike-specific T cells response were compared. Any, local and systemic reactions were pooled by meta-analysis for comparison. Results Of 14,571 records identified, 13 studies (3024 participants) were included for analysis. Compared with homologous BNT/BNT vaccination, heterologous ChAd/BNT schedule probably induced noninferior anti-spike protein while higher neutralizing antibody and better T cells response. Heterologous ChAd/BNT (mRNA-1273) immunisation induced superior anti-spike protein and higher neutralizing antibody and better T cells response compared with homologous ChAd/ChAd vaccination. Heterologous ChAd/BNT (mRNA-1273) had similar risk of any reaction (RR = 1.30, 95% CI: 0.86−1.96) while higher risk of local reactions (RR = 1.65, 95% CI: 1.27−2.15) and systemic reactions (RR = 1.49, 95% CI: 1.17−1.90) compared with homologous ChAd/ChAd vaccination. There was a higher risk of local reactions (RR = 1.16, 95% CI: 1.03−1.31) in heterologous ChAd/BNT (mRNA-1273) vaccination compare with homologous BNT/BNT but a similar risk of any reaction (RR = 1.03, 95% CI: 0.79−1.34) and systemic reactions (RR = 0.89, 95% CI: 0.60−1.30). Conclusions Heterologous ChAd/BNT schedule induced at least comparable immunogenicity compared with homologous BNT/BNT and better immunogenicity compared with homologous ChAd/ChAd vaccination. The synthetical evidence supported the general application of heterologous ...
Keywords	Homologous vaccination ; Heterologous vaccination ; COVID-19 ; Immunogenicity ; Safety ; Infectious and parasitic diseases ; RC109-216 ; Public aspects of medicine ; RA1-1270
Subject code	669
Language	English
Publishing date	2022-05-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The variability of volatile organic compounds during a persistent fog-haze episode

Yue Zhao / Jingjing Lv / Yue Zhou / Junlin An / Bin Zhu

Frontiers in Environmental Science, Vol

2022 Volume 10

Abstract: A persistent fog-haze process associated with high pollution occurred in the northern suburbs of Nanjing from November to December 2013. Based on the comprehensive chemical and microphysical observations during the intense observation period, the ... ...

Abstract	A persistent fog-haze process associated with high pollution occurred in the northern suburbs of Nanjing from November to December 2013. Based on the comprehensive chemical and microphysical observations during the intense observation period, the composition characteristics, and variation rules of volatile organic compounds (VOCs) in the atmosphere under four weather conditions (slight haze, haze, fog, and dense fog) were compared and analyzed, the influencing factors for VOCs during extremely dense fog were discussed in more detail. The average concentrations of VOCs displayed as alkanes > aromatics > alkenes > alkynes, and their concentrations were ranked as dense fog > fog > haze > slight haze, the main factor contributing to the difference in concentrations of VOCs under different weather conditions is the boundary layer characteristics and photochemical reaction rate. Microphysical parameters such as liquid water content (LWC) were negatively correlated with VOCs concentration in dense fog (LWC>0.008 g m−3). Also, the concentration of VOCs showed an oscillating decrease in extremely dense fog (LWC>0.12 g m−3), and the total VOCs removal rate was close to 30%, which may be attributed to an indirect/direct removal effect, in which the enhanced collision and deposition of fog droplets promote the redistribution of VOCs gas-aqueous/particle partitioning, and remove them from the atmosphere by fog water.
Keywords	volatile organic compounds (VOCs) ; fog-haze transformation ; extremely dense fog ; wet scavenge ; microphysical processes ; Environmental sciences ; GE1-350
Subject code	660
Language	English
Publishing date	2022-11-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Characteristics of Raindrop Size Distributions in the Southwest Mountain Areas of China According to Seasonal Variation and Rain Types

Haopeng Wu / Shengjie Niu / Yue Zhou / Jing Sun / Jingjing Lv / Yixiao He

Remote Sensing, Vol 15, Iss 1246, p

2023 Volume 1246

Abstract: The precipitation and raindrop size distribution (RSD) characteristics of the four seasons and different rain types were studied using a PARSIVEL 2 raindrop disdrometer set in the southwest mountain areas of China from 2019 to 2021. The seasonal ... ...

Abstract	The precipitation and raindrop size distribution (RSD) characteristics of the four seasons and different rain types were studied using a PARSIVEL 2 raindrop disdrometer set in the southwest mountain areas of China from 2019 to 2021. The seasonal precipitation in the southwest mountain areas was mainly stratiform rain. The peaks of the RSD were about 1–2 orders of magnitude higher than those in the plains. The convective rain in spring and autumn was very close to the ocean-like convective mass. The local shape–slope ( μ –Λ), radar reflectivity–rain rate ( Z – R ), and kinetic energy–rain rate ( KE – R ) relationships were further derived, and the diversity of these relationships was mainly due to the variability of the RSDs. In addition, the differences in the RSD characteristics between the top and the foot of the mountain during a typical precipitation process in the summer of 2020 were further compared. It was found that the number density of the small particles at the top of the mountain was higher than that at the foot of the mountain due to the broken large raindrops caused by the high wind speed, while the high evaporation rate, strong convective available potential energy (CPAE), and water vapor content at the foot of the mountain could strengthen the RSD, making the number density of the large raindrops at the foot of the mountain higher than that at the top.
Keywords	southwest mountain areas ; raindrop size distribution (RSD) ; seasonal variation ; rain types ; different heights ; Science ; Q
Subject code	550
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Deep learning models for hepatitis E incidence prediction leveraging meteorological factors.

Yi Feng / Xiya Cui / Jingjing Lv / Bingyu Yan / Xin Meng / Li Zhang / Yanhui Guo

PLoS ONE, Vol 18, Iss 3, p e

2023 Volume 0282928

Abstract: Background Infectious diseases are a major threat to public health, causing serious medical consumption and casualties. Accurate prediction of infectious diseases incidence is of great significance for public health organizations to prevent the spread of ...

Abstract	Background Infectious diseases are a major threat to public health, causing serious medical consumption and casualties. Accurate prediction of infectious diseases incidence is of great significance for public health organizations to prevent the spread of diseases. However, only using historical incidence data for prediction can not get good results. This study analyzes the influence of meteorological factors on the incidence of hepatitis E, which are used to improve the accuracy of incidence prediction. Methods We extracted the monthly meteorological data, incidence and cases number of hepatitis E from January 2005 to December 2017 in Shandong province, China. We employ GRA method to analyze the correlation between the incidence and meteorological factors. With these meteorological factors, we achieve a variety of methods for incidence of hepatitis E by LSTM and attention-based LSTM. We selected data from July 2015 to December 2017 to validate the models, and the rest was taken as training set. Three metrics were applied to compare the performance of models, including root mean square error(RMSE), mean absolute percentage error(MAPE) and mean absolute error(MAE). Results Duration of sunshine and rainfall-related factors(total rainfall, maximum daily rainfall) are more relevant to the incidence of hepatitis E than other factors. Without meteorological factors, we obtained 20.74%, 19.50% for incidence in term of MAPE, by LSTM and A-LSTM, respectively. With meteorological factors, we obtained 14.74%, 12.91%, 13.21%, 16.83% for incidence, in term of MAPE, by LSTM-All, MA-LSTM-All, TA-LSTM-All, BiA-LSTM-All, respectively. The prediction accuracy increased by 7.83%. Without meteorological factors, we achieved 20.41%, 19.39% for cases in term of MAPE, by LSTM and A-LSTM, respectively. With meteorological factors, we achieved 14.20%, 12.49%, 12.72%, 15.73% for cases, in term of MAPE, by LSTM-All, MA-LSTM-All, TA-LSTM-All, BiA-LSTM-All, respectively. The prediction accuracy increased by 7.92%. More detailed results are ...
Keywords	Medicine ; R ; Science ; Q
Subject code	333
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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Article ; Online: Effect of Fulvic Acid on the Denitrification in Deep Subsurface Wastewater Infiltration System

Jingjing Lv / Jingjing Li / Yanyan Dou / Guoke Chen / Yubing Ye and Li’an Hou

Nature Environment and Pollution Technology, Vol 22, Iss 4, Pp 2129-

2023 Volume 2136

Abstract: This work aims to explore the impact of fulvic acid (FA) on denitrification within the purification process of sewage in the deep subsurface wastewater infiltration system (DSWIS). In the system, an organic glass column (height = 2.40 m; radius = 0.30 m) ...

Abstract	This work aims to explore the impact of fulvic acid (FA) on denitrification within the purification process of sewage in the deep subsurface wastewater infiltration system (DSWIS). In the system, an organic glass column (height = 2.40 m; radius = 0.30 m) was filled with several layers of soil. Simulated domestic wastewater and extracted FA from landfill leachate were used in the experiments. It was found that before and after the addition of FA, COD, and NH4+-N were efficiently removed when a hydraulic load was 8 cm·d-1. Moreover, after FA addition, the removal efficiency of TN was enhanced from 67.74% to 78.01%. Organic matter transformation analysis indicated that in the under part, the shortage of carbon sources limited the denitrification prior to FA addition, resulting in a low TN removal efficiency. However, after adding FA, more FA-like substances were transferred into protein-like matters than before the addition of FA, which has helped produce more easily biodegradable organics for denitrification. So, the addition of FA could enhance the denitrification process in the system of DSWIS.
Keywords	denitrification ; organic composition ; wastewater infiltration system ; fluorescence analysis ; Environmental effects of industries and plants ; TD194-195 ; Science (General) ; Q1-390
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	Technoscience Publications
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Study on the Characteristics of Traditional Chinese Medicine Syndromes in Patients with Erosive Gastritis Based on Metabolomics.

Shixiong, Zhang / Shaowei, Liu / Zeqi, Yang / Miaochan, Xu / Pingping, Zhou / Haiyan, Bai / Jingjing, Lv / Yangang, Wang

International journal of analytical chemistry

2024 Volume 2024, Page(s) 6684677

Abstract: According to traditional Chinese medicine theory, tongue coatings reflect changes in the body. The goal of this study was to identify a metabolite or a set of metabolites capable of classifying characteristics of traditional Chinese medicine syndromes in ...

Abstract	According to traditional Chinese medicine theory, tongue coatings reflect changes in the body. The goal of this study was to identify a metabolite or a set of metabolites capable of classifying characteristics of traditional Chinese medicine syndromes in erosive gastritis. In this study, we collected tongue coatings of patients with erosive gastritis with damp-heat syndrome (DHS), liver depression and qi stagnation syndrome (LDQSS), and healthy volunteers. Then, we analyzed the differences in metabolic characteristics between the two groups based on metabolomics. We identified 14 potential biomarkers related to the DHS group, and six metabolic pathways were enriched. The differential pathways included pyrimidine metabolism, pantothenate and CoA biosynthesis, citrate cycle (TCA cycle), pyruvate metabolism, glycolysis/gluconeogenesis, and purine metabolism. Similarly, in the LDQSS group, we identified 25 potential biomarkers and 18 metabolic pathways were enriched. The top five pathways were the TCA cycle, sphingolipid metabolism, fatty acid biosynthesis, pantothenate and CoA biosynthesis, and the pentose phosphate pathway. In conclusion, the DHS group and the LDQSS group have different characteristics.
Language	English
Publishing date	2024-01-02
Publishing country	Egypt
Document type	Journal Article
ZDB-ID	2494714-3
ISSN	1687-8779 ; 1687-8760
ISSN (online)	1687-8779
ISSN	1687-8760
DOI	10.1155/2024/6684677
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: The Preparation of N-Doped Titanium Dioxide Films and Their Degradation of Organic Pollutants

Yanyan Dou / Yixuan Chang / Xuejun Duan / Leilei Fan / Bo Yang / Jingjing Lv

International Journal of Environmental Research and Public Health, Vol 19, Iss 15721, p

2022 Volume 15721

Abstract: N-doped TiO 2 films supported by glass slides showed superior photocatalytic efficiency compared with naked TiO 2 powder due to them being easier to separate and especially being responsive to visible light. The films in this study were prepared via the ... ...

Abstract	N-doped TiO 2 films supported by glass slides showed superior photocatalytic efficiency compared with naked TiO 2 powder due to them being easier to separate and especially being responsive to visible light. The films in this study were prepared via the sol–gel method using TBOT hydrolyzed in an ethanol solution and the nitrogen was provided by cabamide. The N-doped TiO 2 coatings were prepared via a dip-coating method on glass substrates (30 × 30 × 2 mm) and then annealed in air at 490 °C for 3 h. The samples were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM) and UV-vis. The doping rate of N ranged from 0.1 to 0.9 (molar ratio), which caused redshifts to a longer wavelength as seen in the UV-vis analysis. The photocatalytic activity was investigated in terms of the degradation of phenol under both UV light and visible light over 4 h. Under UV light, the degradation rate of phenol ranged from 86% to 94% for all the samples because of the sufficient photon energy from the UV light. Meanwhile, under visible light, a peak appeared at the N-doping rate of 0.5, which had a degrading efficiency that reached 79.2%, and the lowest degradation rate was 32.9%. The SEM, XRD and UV-vis experimental results were consistent with each other.
Keywords	photocatalysis ; N-doped films ; visible light ; titanium dioxide ; Medicine ; R
Subject code	620
Language	English
Publishing date	2022-11-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Prediction of hepatitis E using machine learning models.

Yanhui Guo / Yi Feng / Fuli Qu / Li Zhang / Bingyu Yan / Jingjing Lv

PLoS ONE, Vol 15, Iss 9, p e

2020 Volume 0237750

Abstract: Background Accurate and reliable predictions of infectious disease can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task. However, ... ...

Abstract	Background Accurate and reliable predictions of infectious disease can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task. However, for different data series, the performance of these models varies. Hepatitis E, as an acute liver disease, has been a major public health problem. Which model is more appropriate for predicting the incidence of hepatitis E? In this paper, three different methods are used and the performance of the three methods is compared. Methods Autoregressive integrated moving average(ARIMA), support vector machine(SVM) and long short-term memory(LSTM) recurrent neural network were adopted and compared. ARIMA was implemented by python with the help of statsmodels. SVM was accomplished by matlab with libSVM library. LSTM was designed by ourselves with Keras, a deep learning library. To tackle the problem of overfitting caused by limited training samples, we adopted dropout and regularization strategies in our LSTM model. Experimental data were obtained from the monthly incidence and cases number of hepatitis E from January 2005 to December 2017 in Shandong province, China. We selected data from July 2015 to December 2017 to validate the models, and the rest was taken as training set. Three metrics were applied to compare the performance of models, including root mean square error(RMSE), mean absolute percentage error(MAPE) and mean absolute error(MAE). Results By analyzing data, we took ARIMA(1, 1, 1), ARIMA(3, 1, 2) as monthly incidence prediction model and cases number prediction model, respectively. Cross-validation and grid search were used to optimize parameters of SVM. Penalty coefficient C and kernel function parameter g were set 8, 0.125 for incidence prediction, and 22, 0.01 for cases number prediction. LSTM has 4 nodes. Dropout and L2 regularization parameters were set 0.15, 0.001, respectively. By the metrics of RMSE, we obtained 0.022, 0.0204, 0.01 for incidence ...
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2020-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Prediction of hepatitis E using machine learning models

Yanhui Guo / Yi Feng / Fuli Qu / Li Zhang / Bingyu Yan / Jingjing Lv / Tao Song

PLoS ONE, Vol 15, Iss

2020 Volume 9

Abstract	Background Accurate and reliable predictions of infectious disease can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task. However, for different data series, the performance of these models varies. Hepatitis E, as an acute liver disease, has been a major public health problem. Which model is more appropriate for predicting the incidence of hepatitis E? In this paper, three different methods are used and the performance of the three methods is compared. Methods Autoregressive integrated moving average(ARIMA), support vector machine(SVM) and long short-term memory(LSTM) recurrent neural network were adopted and compared. ARIMA was implemented by python with the help of statsmodels. SVM was accomplished by matlab with libSVM library. LSTM was designed by ourselves with Keras, a deep learning library. To tackle the problem of overfitting caused by limited training samples, we adopted dropout and regularization strategies in our LSTM model. Experimental data were obtained from the monthly incidence and cases number of hepatitis E from January 2005 to December 2017 in Shandong province, China. We selected data from July 2015 to December 2017 to validate the models, and the rest was taken as training set. Three metrics were applied to compare the performance of models, including root mean square error(RMSE), mean absolute percentage error(MAPE) and mean absolute error(MAE). Results By analyzing data, we took ARIMA(1, 1, 1), ARIMA(3, 1, 2) as monthly incidence prediction model and cases number prediction model, respectively. Cross-validation and grid search were used to optimize parameters of SVM. Penalty coefficient C and kernel function parameter g were set 8, 0.125 for incidence prediction, and 22, 0.01 for cases number prediction. LSTM has 4 nodes. Dropout and L2 regularization parameters were set 0.15, 0.001, respectively. By the metrics of RMSE, we obtained 0.022, 0.0204, 0.01 for incidence ...
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2020-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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