Assessment of drinking water quality and health risk using water quality index and multiple computational models: a case study of Yangtze River in suburban areas of Wuhan, central China, from 2016 to 2021.

Water quality, increasingly recognized for its significant impact on health, is garnering heightened attention. Previous studies were limited by the number of water quality indicators and the duration of analysis. This study assessed the drinking water quality and its associated health risk in suburban areas of Wuhan, a city in central China, from 2016 to 2021. We collected 368 finished water samples and 1090 tap water samples and tested these for 37 different indicators. The water quality was evaluated using the water quality index, with trends over time analyzed via the Mann-Kendall test. Furthermore, an artificial neural network model was employed for future water quality prediction. Our findings indicated that the water quality in rural Wuhan was generally good and had an improvement from 2016 to 2021. The qualification and excellent rates were 98.91% and 86.81% for finished water, and 97.89% and 78.07% for tap water, respectively. The drinking water quality was predicted to maintain satisfactory in 2022 and 2023. Additionally, principal component analysis revealed that the primary sanitary issues in the water were poor sensory properties, elevated metal contents, high levels of dissolved solids, and microbial contamination. These issues were likely attributable to domestic and industrial waste discharge and aging water pipelines. The health risks associated with the long-term consumption of this water have been steadily decreasing over the years, underscoring the effectiveness of Wuhan's ongoing water management efforts.

Predicting carbon futures prices based on a new hybrid machine learning: Comparative study of carbon prices in different periods.

Accurate prediction of carbon price is of great significance to national energy security and climate environment policies. This paper comes up with a new forecasting model variational mode decomposition, convolutional neural network, bidirectional long short-term memory, and multi-layer perceptron (VMD-CNN-BILSTM-MLP) to predict EUA carbon futures prices in two periods of five years before and after the introduction of emission reduction policies. The parameters of the VMD model are determined by genetic algorithm (GA) firstly, carbon futures prices are broken down into subsequences of different frequencies using the model. The MLP model is then applied to predict the highest frequency sequence. The CNN-BILSTM model is applied to predict other subsequences later. Finally, the predicted values of each subsequence are linearly added to obtain the final result of the entire model. The prediction effect of the model is mainly tested by root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), coefficient of determination (R2) and the modification of Diebold-Mariano test (MDM). In both periods, the proposed model predicts better than the other models, and the prediction effect of carbon futures price in the first five years is a little better than that in the second five years. In general, the experiment of predicting carbon futures prices in two different periods, the experiment of changing the proportion of data set and the experiment of predicting the whole sample all prove that the mixed model proposed in this paper has good prediction effect.

Comparative assessment on ammonia nitrogen adsorption onto a saline soil-groundwater environment: distribution, multi-factor interaction, and optimization using response surface methodology and artificial neural network.

The adsorption of soil can reduce the leaching of NH4+-N from the external environment into groundwater. The adsorption of NH4+-N is affected by many factors. It is critical to use statistical model to quantitatively describe the effects of interaction between two or more factors on the system response. In this study, HJ-Biplot was used to analyze the correlation characteristics of soil water, salt, and nitrogen, and the response surface methodology and artificial neural network were used to statistically visualize the interaction between factors, including concentration, total dissolved solids (TDS), temperature, and pH. The results showed that the study soil was a typical saline soil, with maximum soil NH4+-N content of 85.45 mg/kg. For the adsorption experiments of NH4+-N on saline soils, the effects of factors on the adsorption capacity were assessed using the RSM model. The RSM model was coupled with an ANN to predict the adsorption of NH4+-N by saline soils. The NH4+-N concentration and water pH were both significant at a linear level (p < 0.0001). The interaction between NH4+-N concentration and pH was also more significant (p < 0.01). Under optimal conditions (concentration: 800 mg/L; temperature: 24 °C; TDS: 637 mg/L; pH: 7.83), the NH4+-N adsorption capacity was 1650.2 ug/g, which was in general agreement with the calculated values from the Box-Behnken and RSM model. In addition, a statistical error criterion for the model showed that the RSM-ANN model had greater predictive ability than RSM model.

Estimation of ecological footprint based on tourism development indicators using neural networks and multivariate regression.

The ecological footprint has attracted a lot of attention in the top tourism destination countries, and this issue may be worrying. This study aims to estimate the ecological footprint, using such indicators as economic growth, natural resources, human capital, and the number of tourists in top tourism destination countries. For this purpose, artificial neural network models and multivariate regression were used for a period of 24 years (1995-2019). The results of the study showed a significant positive correlation between economic growth and ecological footprint. Multivariate regression estimation (R = 0.75) is weaker than neural network models (R = 96.3). Regarding predicting the ecological footprint, neural network models have better performance in comparison with the multivariate regression statistical methods. Accordingly, one can say that for planning ecological footprint, deeper look at neural networks can be more effective in predicting top tourism destination countries.

A hybrid of long short-term memory neural network and autoregressive integrated moving average model in forecasting HIV incidence and morality of post-neonatal population in East Asia: global burden of diseases 2000-2019.

BACKGROUND: To forecast the human immunodeficiency virus (HIV) incidence and mortality of post-neonatal population in East Asia including North Korea, South Korea, Mongolia, Japan and China Mainland and Taiwan province. METHODS: The data on the incidence and mortality of HIV in post-neonatal population from East Asia were obtained from the Global Burden of Diseases (GBD). The morbidity and mortality of post-neonatal HIV population from GBD 2000 to GBD 2013 were applied as the training set and the morbidity and mortality from GBD 2014 to GBD 2019 were used as the testing set. The hybrid of ARIMA and LSTM model was used to construct the model for assessing the morbidity and mortality in the countries and territories of East Asia, and predicting the morbidity and mortality in the next 5 years. RESULTS: In North Korea, the incidence and mortality of HIV showed a rapid increase during 2000-2010 and a gradual decrease during 2010-2019. The incidence of HIV was predicted to be increased and the mortality was decreased. In South Korea, the incidence was increased during 2000-2010 and decreased during 2010-2019, while the mortality showed fluctuant trend. As predicted, the incidence of HIV in South Korea might be increased and the mortality might be decreased during 2020-2025. In Mongolia, the incidence and mortality were slowly decreased during 2000-2005, increased during 2005-2015, and rapidly decreased till 2019. The predicted incidence and mortality of HIV showed a decreased trend. As for Japan, the incidence of HIV was rapidly increased till 2010 and then decreased till 2015. The predicted incidence of HIV in Japan was gradually increased. The mortality of HIV in Japan was fluctuant during 2000-2019 and was slowly decreased as predicted. The incidence and mortality of HIV in Taiwan during 2000-2019 was increased on the whole. The predicted incidence of HIV during was stationary and the mortality was decreased. In terms of China Mainland, the incidence and mortality of HIV was fluctuant during 2000-2019. The predicted incidence of HIV in China Mainland was stationary while the mortality was rapidly decreased. CONCLUSION: On the whole, the incidence of HIV combined with other diseases in post-neonatal population was increased before 2010 and then decreased during 2010-2019 while the mortality of those patients was decreased in East Asia.

Quality assessment and prediction of municipal drinking water using water quality index and artificial neural network: A case study of Wuhan, central China, from 2013 to 2019.

The sanitary security of drinking water is closely related to human health, but its quality assessment mainly focused on limited types of indicators and relatively restricted time span. The current study was aimed to evaluate the long-term spatial-temporal distribution of municipal drinking water quality and explore the origin of water contamination based on multiple water indicators of 137 finished water samples and 863 tap water samples from Wuhan city, China. Water quality indexes (WQIs) were calculated to integrate the measured indicators. WQIs of the finished water samples ranged from 0.24 to 0.92, with the qualification rate and excellent rate of 100 % and 96.4 %, respectively, while those of the tap water samples ranged from 0.09 to 3.20, with the qualification rate of 99.9 %, and excellent rate of 95.5 %. Artificial neural network model was constructed based on the time series of WQIs from 2013 to 2019 to predict the water quality thereafter. The predicted WQIs of finished and tap water in 2020 and 2021 qualified on the whole, with the excellent rate of 87.5 % and 92.9 %, respectively. Except for three samples exceeding the limits of free chlorine residual, chloroform and fluoride, respectively, the majority of indicators reached the threshold values for drinking. Our study suggested that municipal drinking water quality in Wuhan was generally stable and in line with the national hygiene standards. Moreover, principal component analysis illustrated that the main potential sources of drinking water contamination were inorganic salts and organic matters, followed by pollution from distribution systems, the use of aluminum-containing coagulants and turbidity involved in water treatment, which need more attention.

Research on the Pathway of Green Financial System to Implement the Realization of China's Carbon Neutrality Target.

To answer to global climate change, promote climate governance and map out a grand blueprint for sustainable development, carbon neutrality has become the target and vision of all countries. Green finance is a means to coordinate economic development and environmental governance. This paper mainly studies the trend of carbon emission reduction in China in the next 40 years under the influence of green finance development and how to develop and improve China's green finance system to help China achieve the goal of "carbon neutrality by 2060". The research process and conclusions are as follows: (1) Through correlation test and data analysis, it is concluded that the development of green finance is an important driving force to achieve carbon neutrality. (2) The grey prediction GM (1,1) model is used to forecast the data of carbon dioxide emissions, green credit balance, green bond issuance scale and green project investment in China from 2020 to 2060. The results show that they will all increase year by year in the next 40 years. (3) BP neural network model is used to further predict carbon dioxide emissions from 2020 to 2060. It is expected that China's CO2 emissions will show an "inverted V" trend in the next 40 years, and China is expected to achieve a carbon peak in 2032 and be carbon neutral in 2063. Based on the results of the research above, this paper provides a supported path of implementing the realization of the carbon-neutral target of China from the perspective of developing and improving green financial system, aiming to provide references for China to realize the vision of carbon neutrality, providing policy suggestions for relevant departments, and provide ideas for other countries to accelerate the realization of carbon neutrality.

A fuzzy DRBFNN-based information security risk assessment method in improving the efficiency of urban development.

The rapid development of urban informatization is an important way for cities to achieve a higher pattern, but the accompanying information security problem become a major challenge restricting the efficiency of urban development. Therefore, effective identification and assessment of information security risks has become a key factor to improve the efficiency of urban development. In this paper, an information security risk assessment method based on fuzzy theory and neural network technology is proposed to help identify and solve the information security problem in the development of urban informatization. Combined with the theory of information ecology, this method establishes an improved fuzzy neural network model from four aspects by using fuzzy theory, neural network model and DEMATEL method, and then constructs the information security risk assessment system of smart city. According to this method, this paper analyzed 25 smart cities in China, and provided suggestions and guidance for information security control in the process of urban informatization construction.

An Empirical Study on the Equity Performance of China's Health Insurance Companies During the COVID-19 Pandemic-Based on Cases of Dominant Listed Companies.

The health insurance industry in China is undergoing great shocks and profound impacts induced by the worldwide COVID-19 pandemic. Taking for instance the three dominant listed companies, namely, China Life Insurance, Ping An Insurance, and Pacific Insurance, this paper investigates the equity performances of China's health insurance companies during the pandemic. We firstly construct a stock price forecasting methodology using the autoregressive integrated moving average, back propagation neural network, and long short-term memory (LSTM) neural network models. We then empirically study the stock price performances of the three listed companies and find out that the LSTM model does better than the other two based on the criteria of mean absolute error and mean square error. Finally, the above-mentioned models are used to predict the stock price performances of the three companies.

Scenario simulation of the EU carbon price and its enlightenment to China.

Through exploring price characteristics of carbon futures products in EU ET, this paper aims to provide China's policy makers with meaningful materials and references for understanding how a carbon trading market can be established and well regulated. Based on the dataset comprising of multiple sources including Euro stoxx600 index, coal and crude oil prices, natural gas prices and European clean energy company stock prices, etc., this paper uses BP neural network model to simulate the long-term trends of carbon futures prices in six scenarios that represent the typical features of a carbon trading market. The results show that: (1) the magnitude of economic development's effect on carbon price is the largest among other factors, with the shortest duration; (2) in comparison, the effect of black energy consumption is weaker, but its lasting duration is the longest; (3) the impact of clean energy development on carbon price is similar to that of black energy, but the effect magnitude and lasting duration are relatively smaller. These findings suggest three viable directions for the development of China's carbon trading market in future i.e. adjusting total quotas in accordance with economic development, establishing market price stabilization mechanism, and developing clean energy. The novelty of this paper is to simulate the long-term trend of carbon prices by constructing a carbon price prediction system.

A novel approach to the diagnostic assessment of carpal tunnel syndrome based on the frequency domain of the compound muscle action potential.

Conventional electrophysiological (EP) tests may yield ambiguous or false-negative results in some patients with signs and symptoms of carpal tunnel syndrome (CTS). Therefore, researchers tend to investigate new parameters to improve the sensitivity and specificity of EP tests. We aimed to investigate the mean and maximum power of the compound muscle action potential (CMAP) as a novel diagnostic parameter, by evaluating diagnosis and classification performance using the supervised Kohonen self-organizing map (SOM) network models. The CMAPs were analyzed using the fast Fourier transform (FFT). The mean and maximum power parameters were calculated from the power spectrum. A counter-propagation artificial neural network (CPANN), supervised Kohonen network (SKN) and XY-fused network (XYF) were compared to evaluate the classification and diagnostic performance of the parameters using the confusion matrix. The mean and maximum power of the CMAP were significantly lower in patients with CTS than in the normal group (p < 0.05), and the XYF network had the best total performance of classification with 91.4%. This study suggests that the mean and maximum power of the CMAP can be considered as less time-consuming parameters for the diagnosis of CTS without using additional EP tests which can be uncomfortable for the patient due to poor tolerance to electrical stimulation.