Stacked Hybridization to Enhance the Performance of Artificial Neural Networks (ANN) for Prediction of Water Quality Index in the Bagh River Basin, India

Data availability statement: The data pertaining to this study have not been deposited in a publicly accessible repository, given that all relevant data are thoroughly detailed in the article or appropriately cited in the manuscript.Water quality assessment is paramount for environmental monitoring and resource management, particularly in regions experiencing rapid urbanization and industrialization. This study introduces Artificial Neural Networks (ANN) and its hybrid machine learning models, namely ANN-RF (Random Forest), ANN-SVM (Support Vector Machine), ANN-RSS (Random Subspace), ANN-M5P (M5 Pruned), and ANN-AR (Additive Regression) for water quality assessment in the rapidly urbanizing and industrializing Bagh River Basin, India. The Relief algorithm was employed to select the most influential water quality input parameters, including Nitrate (NO3-), Magnesium (Mg2+), Sulphate (SO42-), Calcium (Ca2+), and Potassium (K+). The comparative analysis of developed ANN and its hybrid models was carried out using statistical indicators (i.e., Nash-Sutcliffe Efficiency (NSE), Pearson Correlation Coefficient (PCC), Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Root Square Error (RRSE), Relative Absolute Error (RAE), and Mean Bias Error (MBE) and graphical representations (i.e., Taylor diagram). Results indicate that the integration of support vector machine (SVM) with ANN significantly improves performance, yielding impressive statistical indicators: NSE (0.879), R2 (0.904), MAE (22.349), and MBE (12.548). The methodology outlined in this study can serve as a template for enhancing the predictive capabilities of ANN models in various other environmental and ecological applications, contributing to sustainable development and safeguarding natural resources.No funding was received for conducting this study

Thermally activated delayed fluorescence emitters in light-emitting electrochemical cells

Thermally activated delayed fluorescence (TADF) represents a very promising singlet harvesting mechanism that permits harvesting of both singlet and triplet excitons in electroluminescent devices. In this chapter, the operating principle of TADF mechanism is introduced. Two major classes of TADF emitters employed in light-emitting electrochemical cell (LEC) devices, small molecule organic compounds, and copper(I) complexes, are discussed in the context of their optoelectronic properties and LEC device performance metrics. A critical outlook for each class of emitters is also provided.</p

Light-Emitting Electrochemical Cells: A Review on Recent Progress

The light-emitting electrochemical cell (LEC) is an area-emitting device, which features a complex turn-on process that ends with the formation of a p-n junction doping structure within the active material. This in-situ doping transformation is attractive in that it promises to pave the way for an unprecedented low-cost fabrication of thin and light-weight devices that present efficient light emission at low applied voltage. In this review, we present recent insights regarding the operational mechanism, breakthroughs in the development of scalable and adaptable solution-based methods for cost-efficient fabrication, and successful efforts toward the realization of LEC devices with improved efficiency and stability.</p