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Geopolymer concrete (GPC) is an eco-friendly alternative for conventional concrete. It exploits industrial by-products in production to reduce the environmental impact and improve sustainability. This study focuses on envisaging the 28-day compressive strength of cenosphere-based geopolymer concrete incorporating copper slag using Artificial Neural Networks (ANN). The assimilation of ANN models in predicting the compressive strength of cenosphere-based geopolymer concrete with copper slag offers a promising approach to sustainable construction. By precisely forecasting the compressive strength of concrete based on the ingredient proportions, these models can rationalise the design process. The test results signposted that the developed model gives higher accuracy (> 98.6%), capability and flexibility in predicting the compressive strength of geo-polymer concrete incorporated with cenosphere and copper slag.

Background As a kind of widely distributed disease in China, acquired immune deficiency syndrome (AIDS) has been quickly growing each year, become a serious problem and caused serious damage to the life and health of people and the social events of China and the world because of its high fatality rate. It has been much concerned by all aspects of society. Therefore, developing early warning technology and finding the trend of early development are of quite significance to prevent and control human immunodeficiency virus (HIV)/AIDS. This study aimed to explore a suitable model for the morbidity of AIDS in China and establish a professional and feasible disease prediction model for the prevention and control works of AIDS. Methods At present, the traditional linear model is still utilized by most scholars to predict the incidence of HIV/AIDS. In addition, some scholars may attempt to use the nonlinear prediction model. Both prediction models showed good fitting and prediction effects. In China, the incidence of AIDS presents linear and nonlinear characteristics. In this research, the nonlinear back propagation artificial neural network (BP-ANN) model and the typical auto-regressive integrated moving average (ARIMA) linear model were applied to predict the incidence of HIV/AIDS and compare their fitting effects. Results Both models were capable of predicting the expected cases of AIDS. It was seen that ARIMA and BP-ANN models could be used to forecast the monthly incidence of HIV/AIDS, but the fitting and forecasting effects of the nonlinear BP neural network model were better than those of the traditional linear ARIMA model. Conclusions In summary, it was further concluded that the BP-ANN model was a suitable way to monitor and predict the change trend and morbidity of AIDS in China.

The ease of use of fuzzy time series methods and their success in forecasting performance have led to a rapid increase in research in this field. While classical fuzzy time series methods operate solely on membership values, intuitionistic fuzzy time series methods are based on both membership and non-membership values. In this study, a new mixed-order single-variable intuitionistic fuzzy time series forecasting method is proposed. The proposed approach integrates an artificial neural network, the intuitionistic fuzzy c-means algorithm, and the grey wolf optimization algorithm. The intuitionistic fuzzy time series is constructed using crisp values, membership degrees, and non-membership degrees. Fuzzy relationships are determined through a novel artificial neural network based on the dendritic neuron model and optimized using the grey wolf optimization algorithm. Forecasting models are developed separately based on membership and non-membership values, and the final forecasts are obtained by combining these models using weights determined by the grey wolf optimization algorithm. The performance of the proposed method is evaluated and compared with several existing fuzzy time series methods from the literature using different real-world time series datasets.

The present work is focused on modeling and predicting the cumulative number of deaths from COVID-19 in México by comparing an artificial neural network (ANN) with a Gompertz model applying multiple optimization algorithms for the estimation of coefficients and parameters, respectively. For the modeling process, the data published by the daily technical report COVID-19 in Mexico from March 19th to September 30th were used. The data published in the month of October were included to carry out the prediction. The results show a satisfactory comparison between the real data and those obtained by both models with a R 2  > 0.999. The Levenberg–Marquardt and BFGS quasi-Newton optimization algorithm were favorable for fitting the coefficients during learning in the ANN model due to their fast and precision, respectively. On the other hand, the Nelder–Mead simplex algorithm fitted the parameters of the Gompertz model faster by minimizing the sum of squares. Therefore, the ANN model better fits the real data using ten coefficients. However, the Gompertz model using three parameters converges in less computational time. In the prediction, the inverse ANN model was solved by a genetic algorithm obtaining the best precision with a maximum error of 2.22% per day, as opposed to the 5.48% of the Gompertz model with respect to the real data reported from November 1st to 15th. Finally, according to the coefficients and parameters obtained from both models with recent data, a total of 109,724 cumulative deaths for the inverse ANN model and 100,482 cumulative deaths for the Gompertz model were predicted for the end of 2020.

Better accuracy in short-term forecasting is required for intermediate planning for the national target to reduce CO2 emissions. High stake climate change conventions need accurate predictions of the future emission growth path of the participating countries to make informed decisions. The current study forecasts the CO2 emissions of the 17 key emitting countries. Unlike previous studies where linear statistical modeling is used to forecast the emissions, we develop a multilayer artificial neural network model to forecast the emissions. This model is a dynamic nonlinear model that helps to obtain optimal weights for the predictors with a high level of prediction accuracy. The model uses the gross domestic product (GDP), urban population ratio, and trade openness, as predictors for CO2 emissions. We observe an average of 96% prediction accuracy among the 17 countries which is much higher than the accuracy of the previous models. Using the optimal weights and available input data the forecasting of CO2 emissions is undertaken. The results show that high emitting countries, such as China, India, Iran, Indonesia, and Saudi Arabia are expected to increase their emissions in the near future. Currently, low emitting countries, such as Brazil, South Africa, Turkey, and South Korea will also tread on a high emission growth path. On the other hand, the USA, Japan, UK, France, Italy, Australia, and Canada will continuously reduce their emissions. These findings will help the countries to engage in climate mitigation and adaptation negotiations.

In this research, our main goal was to improve power load forecasting accuracy by considering the impact of meteorological factors on the total power of the electrical system, examining existing load data, local weather, wind direction, and other parameters affecting total power load. We divided the data from the past three years into a training dataset, comprising 75% of the data, and a testing dataset with the remaining 25%. We employed a basic machine learning technique (Support Vector Machine) and three distinct neural network approaches (Artificial Neural Network, Convolutional Neural Network, and Long-Short Term Memory Network) to develop analytical models. Through experimentation, the LSTM model achieved a loss value of 0.0034 and required 1426.78 seconds of training time across 100 epochs. Considering the time expense and model complexity, we chose the LSTM model to forecast power load at 15-minute intervals for the subsequent ten days, achieving a satisfactory prediction and fitting outcome. Our results suggest that the LSTM model is a promising method for optimizing performance and reliability in electrical power systems.

The effective estimation of mixed-layer depth (MLD) plays a significant role in the study of ocean dynamics and global climate change. However, the methods of estimating MLD still have limitations due to the sparse resolution of the observed data. In this study, a hybrid estimation method that combines the K-means clustering algorithm and an artificial neural network (ANN) model was developed using sea-surface parameter data in the Indian Ocean as a case study. The oceanic datasets from January 2012 to December 2019 were obtained via satellite observations, Argo in situ data, and reanalysis data. These datasets were unified to the same spatial and temporal resolution (1° × 1°, monthly). Based on the processed datasets, the K-means classifier was applied to divide the Indian Ocean into four regions with different characteristics. For ANN training and testing in each region, the gridded data of 84 months were used for training, and 12-month data were used for testing. The ANN results show that the optimized NN architecture comprises five input variables, one output variable, and four hidden layers, each of which has 40 neurons. Compared with the multiple linear regression model (MLR) with a root-mean-square error (RMSE) of 5.2248 m and the HYbrid-Coordinate Ocean Model (HYCOM) with an RMSE of 4.8422 m, the RMSE of the model proposed in this study was reduced by 27% and 22%, respectively. Three typical regions with high variability in their MLDs were selected to further evaluate the performance of the ANN model. Our results showed that the model could reveal the seasonal variation trend in each of the selected regions, but the estimation accuracy showed room for improvement. Furthermore, a correlation analysis between the MLD and input variables showed that the surface temperature and salinity were the main influencing factors of the model. The results of this study suggest that the pre-clustering ANN method proposed could be used to estimate and analyze the MLD in the Indian Ocean. Moreover, this method can be further expanded to estimate other internal parameters for typical ocean regions and to provide effective technical support for ocean researchers when studying the variability of these parameters.

In cold regions, a better understanding of soil thermal conductivity is necessary for a variety of earthworks and engineering applications such as ground heat exchanger piles and energy piles, installation of underground power cables, and so on. In this study, the effects of the internal factors of the soil such as water content, dry density, porosity, saturation degree, and the external factors of the soil like freeze–thaw cycles and temperatures were studied on the thermal conductivity (λ) of the sandy soil. The λ values of the soil samples were determined at six different volumetric water contents (0.190, 0.212, 0.230, 0.246, 0.260, and 0.320 m3 m−3) and four frozen temperatures (4 °C, − 7 °C, − 12 °C and − 20 °C) under five different numbers of freeze–thaw cycles (0, 2, 5, 9, and 12). Then, new prediction models based on the internal (the ANN-I model) and both internal and external factors (the ANN-G model) of the soil proposed by artificial neural network (ANN) technology. The developed ANN models were compared with three empirical models (Tortuosity-Parallel model, Farouki model, and de Vries model) to verify their reliability and effectiveness. The results showed that dry density, water content, porosity, saturation degree, and temperature have significant and variable influences on the λ of the soil subjected to repeated freeze–thaw cycles. The ANN-G model provided the highest accuracy in predicting the λ with increasing numbers of freeze–thaw cycles. For frozen sandy soil samples subjected to repeated freeze–thaw cycles, the Tortuosity-Parallel model exhibited the best performance, followed by the Farouki model and the de Vries model with the poorest performance.

The red-billed blue magpie optimizer (RBMO) is employed in this research study to address parameter extraction in polymer exchange membrane fuel cells (PEMFCs), along with three recently implemented optimizers. The sum of squared deviations (SSD) between the simulated and measured stack voltages defines the fitness function of the optimization problem under investigation subject to a set of working constraints. Three distinct PEMFCs stacks models—the Ballard Mark, Temasek 1 kW, and Horizon H-12 units—are used to illustrate the applied RBMO’s feasibility in solving this challenge in comparison to other recent algorithms. The highest percentages of biased voltage per reading for the Ballard Mark V, Temasek 1 kW, and Horizon H-12 are, respectively, +0.65%, +0.20%, and −0.14%, which are negligible errors. The primary characteristics of PEMFC stacks under changing reactant pressures and cell temperatures are used to evaluate the precision of the cropped optimized parameters. In the final phase of this endeavor, the sensitivity of the cropped parameters to the PEMFCs model’s performance is investigated using two machine learning techniques, namely, artificial neural network and Gaussian process regression models. The simulation results demonstrate that the RBMO approach extracts the PEMFCs’ appropriate parameters with high precision.