Artificial Neural Network Model Research Articles

Enhancement of mechanical and tribological properties in glass fiber-reinforced polymer composites with multi-walled carbon nanotubes and ANN-based COF prediction

ABSTRACT The study investigated the effects of incorporating multi-walled carbon nanotubes (MWCNTs) into glass fiber-reinforced polymer composites on their mechanical and tribological properties. Experimental results demonstrated significant enhancements across various parameters. Microhardness increased by 25.03%, attributed to improved interfacial bonding and load transfer facilitated by MWCNTs. Tensile strength and modulus improved by 10.58% and 28.2%, respectively, indicating reinforced load-bearing capacity. Flexural strength and modulus increased by 33.78% and 36.29%, respectively, due to enhanced interfacial bonding. Inter-laminar shear strength (ILSS) exhibited a notable increase of 20.79% with MWCNT incorporation. Ity mpact toughness improved by 19.41% in Izod and 32.32% in Charpy tests, attributed to MWCNTs’ energy-dissipating properties and enhanced interfacial bonding. Tribological tests showed decreased wear and friction coefficients, along with reduced debris formation and fiber breakage in SEM analysis, indicating improved bonding between the matrix and fibers. Furthermore, an artificial neural network (ANN) model accurately predicted the coefficient of friction, aligning well with experimental values, offering potential for predictive modeling in composite material analysis. Overall, the incorporation of MWCNTs significantly enhanced mechanical properties and wear resistance of the composite material.

Long short‐term memory‐based forecasting of uncertain parameters in an islanded hybrid microgrid and its energy management using improved grey wolf optimization algorithm

AbstractAn islanded hybrid AC‐DC microgrid interconnects renewable energy sources, distributed generators, and energy storage, primarily for remote areas without grid access. Its reliability depends on variable renewable output and load demand, while an energy management system optimizes power scheduling and reduces costs. In the first phase of this paper, uncertainty parameters like day‐ahead power from renewable energy sources (RES) and load demand (LD) are forecasted using the long short‐term memory (LSTM) deep learning algorithm. The LSTM outperforms the artificial neural network (ANN) model in terms of mean square error (MSE) and prediction accuracy (R2) for both training and testing datasets. In the second phase, the forecasted RES power and LD are used for optimal distributed generator (DG) scheduling using the improved grey wolf optimization (IGWO) algorithm. The objective of energy management in an islanded hybrid microgrid (HMG) is to minimize daily operating costs by considering load demand and the bidding costs of energy sources and storage devices. Two operational scenarios are evaluated to minimize the operating costs and optimize battery life. The proposed method, validated with IEEE standard test systems, is compared against several metaheuristic techniques. Results demonstrate that the improved grey wolf optimization (IGWO) algorithm is more effective at reducing costs and provides faster optimal solutions.

Numerical analysis of FRP retrofitting in RC beam-column exterior joints at high temperatures and predictive modeling using artificial neural networks

PurposeThe purpose of this study is to perform a numerical analysis of fiber-reinforced polymer (FRP) retrofitting in reinforced concrete (RC) joints at high temperatures and predict models using artificial neural networks (ANN). The aim was to gain insights into their structural behavior across a range of loading conditions from room temperature to 800°C. Additionally, the research assessed the efficiency of carbon fiber-reinforced polymer (CFRP), glass fiber reinforced polymer (GFRP) and aramid fiber reinforced polymer (AFRP) strengthening in enhancing the structural performance of the critical sections.Design/methodology/approachThe linear numerical simulations were conducted to evaluate the performance of RC beam-column joints using finite element modelling (FEM) analysis. The ANN model demonstrated exceptional effectiveness in predicting the stiffness of frames with openings, establishing itself as the premier machine learning algorithm for frame stiffness estimation. In the conventional model, 300°C was proven to be an effective temperature approach. Subsequently, maintaining a constant temperature of 300°C, an in-depth analysis of nearly 30 models of three retrofitting techniques was conducted under thermomechanical loading.FindingsThe CFRP retrofits yielded 15% less deflection and 30% more stress than the remaining FRPs, and the ANN models predicted the deflection, main stresses, bending moment and shear force. The ANN model results were compared with those of other frequently used models. The R thresholds (R = 0.954, 0.981, 0.986, 0.968, 0.978 and 0.936) for training, testing and validation indicated that the ANN model achieved data variability. The findings indicate that the ANN model is more accurate because of the strong connection between the numerical model and the prediction.Originality/valueTo identify the pinpoint of critical segments within the rehabilitation section and determine the most effective wrapping method among the three laminates.

Autoencoder-Driven Training Data Selection Based on Hidden Features for Improved Accuracy of ANN Short-Term Load Forecasting in ADMS

This paper presents a novel methodology for short-term load forecasting in the context of significant shifts in the daily load curve due to the rapid and extensive adoption of Distributed Energy Resources (DERs). The proposed solution, built upon the Similar Days Method (SDM) and Artificial Neural Network (ANN), introduces several novelties: (1) selection of similar days based on hidden representations of day data using Autoencoder (AE); (2) enhancement of model generalization by utilizing a broader set of training examples; (3) incorporating the relative importance of training examples derived from the similarity measure during training; and (4) mitigation of the influence of outliers by applying an ensemble of ANN models trained with different data splits. The presented AE configuration and procedure for selecting similar days generated a higher-quality training dataset, which led to more robust predictions by the ANN model for days with unexpected deviations. Experiments were conducted on actual load data from a Serbian electrical power system, and the results were compared to predictions obtained by the field-proven STLF tool. The experiments demonstrated an improved performance of the presented solution on test days when the existing STLF tool had poor predictions over the past year.

Artificial Neural Network Modeling of the Removal of Methylene Blue Dye Using Magnetic Clays: An Environmentally Friendly Approach

In this study, the effectiveness of Fe3O4-based clay as a cost-effective material for removing methylene blue (MB) dye from aqueous solutions was evaluated. The structural properties of the clay and Fe3O4-based clay were analyzed using SEM, XRF, BET, XRD, FTIR, and TGA techniques. In this research, the effects of various aspects, such as adsorbent amount, contact time, solution pH, adsorption temperature, and initial dye concentration, on the adsorption of Fe3O4-based clay are investigated. The experiments aimed at understanding the adsorption mechanism of Fe3O4-based clay have shown that the adsorption kinetics are accurately described by the pseudo-second order kinetic model, while the equilibrium data are well represented by the Langmuir isotherm model. The maximum adsorption capacity (qm) was calculated as 52.63 mg/g at 25 °C, 53.48 mg/g at 30 °C, and 54.64 mg/g at 35 °C. All variables affecting the MB adsorption process were systematically optimized in a controlled experimental framework. The effectiveness of the artificial neural network (ANN) model was refined by modifying variables such as the quantity of neurons in the latent layer, the number of inputs, and the learning rate. The model’s accuracy was assessed using the mean absolute percentage error (MAPE) for the removal and adsorption percentage output parameters. The coefficient of determination (R2) values for the dyestuff training, validation, and test sets were found to be 99.40%, 92.25%, and 96.30%, respectively. The ANN model demonstrated a mean squared error (MSE) of 0.614565 for the training data. For the validation dataset, the model recorded MSE values of 0.99406 for the training data, 0.92255 for the validation set, and 0.96302 for the test data. In conclusion, the examined Fe3O4-based clays offer potential as effective and cost-efficient adsorbents for purifying water containing MB dye in various industrial settings.

Identification and Analysis of Potential Biomarkers Associated with Neutrophil Extracellular Traps in Cervicitis.

Early diagnosis of cervicitis is important. Previous studies have found that neutrophil extracellular traps (NETs) play pro-inflammatory and anti-inflammatory roles in many diseases, suggesting that they may be involved in the inflammation of the uterine cervix and NETs-related genes may serve as biomarkers of cervicitis. However, what NETs-related genes are associated with cervicitis remains to be determined. Transcriptome analysis was performed using samples of exfoliated cervical cells from 15 patients with cervicitis and 15 patients without cervicitis as the control group. First, the intersection of differentially expressed genes (DEGs) and neutrophil extracellular trap-related genes (NETRGs) were taken to obtain genes, followed by functional enrichment analysis. We obtained hub genes through two machine learning algorithms. We then performed Artificial Neural Network (ANN) and nomogram construction, confusion matrix, receiver operating characteristic (ROC), gene set enrichment analysis (GSEA), and immune cell infiltration analysis. Moreover, we constructed ceRNA network, mRNA-transcription factor (TF) network, and hub genes-drug network. We obtained 19 intersecting genes by intersecting 1398 DEGs and 136 NETRGs. 5 hub genes were obtained through 2 machine learning algorithms, namely PKM, ATG7, CTSG, RIPK3, and ENO1. Confusion matrix and ROC curve evaluation ANN model showed high accuracy and stability. A nomogram containing the 5 hub genes was established to assess the disease rate in patients. The correlation analysis revealed that the expression of ATG7 was synergistic with RIPK3. The GSEA showed that most of the hub genes were related to ECM receptor interactions. It was predicted that the ceRNA network contained 2 hub genes, 3 targeted miRNAs, and 27 targeted lnRNAs, and that 5 mRNAs were regulated by 28 TFs. In addition, 36 small molecule drugs that target hub genes may improve the treatment of cervicitis. In this study, five hub genes (PKM, ATG7, CTSG, RIPK3, ENO1) provided new directions for the diagnosis and treatment of patients with cervicitis.

Estimation on compressive strength of recycled aggregate self-compacting concrete using interpretable machine learning-based models

PurposeRecycled aggregate self-compacting concrete (RASCC) has the potential for sustainable resource utilization and has been widely applied. Predicting the compressive strength (CS) of RASCC is challenging due to its complex composite nature and nonlinear behavior.Design/methodology/approachThis study comprehensively evaluated commonly used machine learning (ML) techniques, including artificial neural networks (ANN), random trees (RT), bagging and random forests (RF) for predicting the CS of RASCC. The results indicate that RF and ANN models typically have advantages with higher R2 values, lower root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE) values.FindingsThe combination of ML and Shapley additive explanation (SHAP) interpretable algorithms provides physical rationality, allowing engineers to adjust the proportion based on parameter analysis to predict and design RASCC. The sensitivity analysis of the ML model indicates that ANN’s interpretation ability is weaker than tree-based algorithms (RT, BG and RF). ML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.Originality/valueML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.

Application of Sentinel-2 Level-2A images for monitoring water surface in reservoirs in the semiarid region of Pernambuco — Brazil

Remote sensing techniques offer effective and efficient alternatives for observing the spatiotemporal dynamics of surface water in reservoirs. This paper aimed to analyze the applicability of Sentinel-2 Level-2A satellite images from 2016 to 2024 for mapping and monitoring the extent of water surfaces in reservoirs in the Sertão region of Pernambuco state. An automatic, unsupervised, and non-parametric algorithm was employed, combining water indices with reflectance bands of optical images to identify water pixels. The results were compared with two datasets: in situ monitoring and MapBiomas. Issues with optical images affected by clouds over the reservoir and errors in classifying water pixels were noted. Generally, the algorithm tended to underestimate the extent of the water surface due to difficulty detecting water pixels at the edges of the reservoirs. To mitigate this issue, an artificial neural network (ANN) was applied to correct the underestimation bias. The bias correction improved the performance of the metrics when the size and representativeness of the calibration sample were sufficient for training and building the ANN model.

Comparative Study of Back-Propagation Artificial Neural Network Models for Predicting Salinity Parameters Based on Spectroscopy Under Different Surface Conditions of Soda Saline–Alkali Soils

Comparative Study of Back-Propagation Artificial Neural Network Models for Predicting Salinity Parameters Based on Spectroscopy Under Different Surface Conditions of Soda Saline–Alkali Soils

Experimental study of the surface finishing of CNC magnetic abrasive finishing based on ANN

ABSTRACT CNC Magnetic Abrasive Finishing (CNC MAF) is famous for aluminium sheet processing. This study used an artificial neural network (ANN) analytical model with a Bayesian regularisation algorithm (BR) to examine how machine parameters (rotation speed, feed rate, depth of cut, and several passes) affect surface roughness during CNC MAF of aluminium sheet. Experimental and quantitative data proved its prediction ability. ANN model to select the best machine parameters for low surface roughness. The perfect numerical solution was experimentally tested, and surface morphology and roughness were used to evaluate processing quality under optimal machine parameters. The ANN model is also compared to Taguchi L16 for prediction and optimisation. Compared to ANN, prediction and optimal surface finishing mean absolute error are 96% lower. The results show that the ANN model is better at studying machine configuration during CNC MAF of Al sheet. SEM and 3D Optical profilometer were also used to analyze the Al sheet. The UV-VIS Spectrophotometer measures Al polished sample reflectance at 200–700 nm.

Compressive strength of nano concrete materials under elevated temperatures using machine learning

In this study, four Artificial intelligence (AI) - based machine learning models were developed to estimate the Residual compressive strength (RCS) value of concrete supported with nano additives of Nanocarbon tubes (NCTs) and Nano alumina (NAl), after exposure to elevated temperatures ranging from 200 to 800 degrees. These models were developed via adapting meta- heuristic models including the Water cycle algorithm (WCA), Genetic algorithm (GA), and classical AI models of Artificial neural networks (ANNs), Fuzzy logic models (FLM), in addition to the statistical method of Multiple linear regression (MLR). 156 post heating experimental results available as a literature data (represents four input parameters of temperature change, heat exposure duration, nanomaterial type, and replacement proportion) are used to achieve the study’s objective. Results of the developed models demonstrated that ANN and FLM have strong potential in predicting RCS. However, it is often infeasible to generate practical equations that relate input and output variables from these models. Upon analysing the results of the WCA and GA, it was found that WCA yielded the most accurate predictions based on all performance indicators. Furthermore, RCS prediction equations with superior accuracy were derived utilizing the meta-heuristic AI models of WCA and GA, with Mean absolute errors (MAEs) of 3.09 kg/cm² and 3.53 kg/cm² for the training, 1.91 kg/cm² and 2.72 kg/cm² for the validation, and 1.91 kg/cm² and 2.72 kg/cm² for the testing data sets, respectively. Additionally, sensitivity analysis via neural networks weights and SHAP investigation were performed to reveals the impact and relationship of the input variables with the output variables. Both techniques reveal that temperature degree and time of exposure had the highest positive impact on RCS value, followed by NAl and NCTs, in order.

Multi-method machine learning techniques in gold pathfinder elements prediction in central parts of Tanzania using stream sediment geochemical data

Prediction models using machine learning techniques have proven to be a reliable technique in mineral exploration. A combination of these techniques is very robust and reliable in exploration targeting and much dependable as an approach in greenfield. In this study, multi-machine learning methods: random forest (RF), support vector machine (SVM), and artificial neural network (ANN) were employed to conduct a data-driven gold (Au) prospectivity modelling in the Central parts of the Tanzania Craton (TC). A total of 166 samples with Au concentrations from stream sediment samples were considered. Based on the modeling results, the RF model demonstrates superior prediction accuracy compared to the SVM (MSE of 0.89) and ANN models (MSE of 1.21), achieving an MSE of less than 0.82. In terms of overall predictive performance and efficiency, the RF model outperforms other ML models deployed in this research. Therefore, it is deemed the suitable model for gold (Au) prediction in the TC catchments. According to the geological interpretation derived from the model, anomalies in arsenic (As), nickel (Ni), and tungsten (W) now emerge as significant predictors in the quest for gold. This implies that the association of As–Ni–W are potential pathfinder elements in the exploration of gold in the central part of the TC.

Exploiting RSM and ANN modeling methods to optimize phosphate ions removal using LDH/alginate composite beads

In the present work, layered double hydroxide (MgAl) and alginate composite beads (LDH/alginate) were developed and used as low-cost and environmentally friendly adsorbent for phosphate ions removal. The successful incorporation of sodium alginate into the LDH structure was confirmed through SEM analysis. The dried beads exhibited a notably rough surface, which promotes molecular movement and potentially enhances pollutant adsorption. The pH of zero charge point (pHPZC) of the composite was found to be 7.41. This result implies that negative ions have a tendency to draw positive charges on the adsorbent surface via electrostatic interaction forces when the pH is lower than the pHPZC. On the other hand, a surface that has a pH higher than pHPZC mostly has a negative charge. The percentage removal and adsorption capacity were investigated as a function of contact time.Experiments were carried out by simultaneously varying three factors, in order to evaluate and compare the predictive capabilities of the response surface methodology (RSM) and the artificial neural network approach (ANN) for the adsorption process. Both methods demonstrated a strong ability to accurately predict the adsorption process. However, the response surface methodology exhibited a lower prediction error compared to the artificial neural network approach.The central composite design within response surface methodology (CCD-RSM) was employed to optimize the experimental conditions for the adsorption process. The model, which considers three factors: adsorbent dose (A), initial concentration (B), and contact time (C), proved to be significant. Among these, the quadratic term (B2) had the most substantial impact on the phosphate ion adsorption rate. The analysis yielded an R2 value of 0.94, indicating an excellent fit to the data. The findings suggest that increasing contact time and reducing the initial concentration improve phosphate ion removal efficiency, while the adsorbent dose has little to no effect. The Artificial Neural Network (ANN) model effectively predicted the adsorptive remediation of phosphate ions onto LDH/alginate composite beads, achieving a high coefficient of determination (R2 = 0.984) between the model outputs and the experimental data. The study highlights the significant potential of LDH/alginate composite beads as a natural adsorbent for the removal of phosphate ions from aqueous solutions. These results underscore the environmental friendliness and efficiency of the adsorbent used for phosphate adsorption.

Predictive modeling of membrane reactor efficiency using advanced artificial neural networks for green hydrogen production

The imperative to decarbonize the energy sector has prompted substantial advancements in clean electricity generation, with hydrogen emerging as a promising low-carbon energy carrier. While hydrogen synthesis from renewable sources is crucial, challenges persist, necessitating innovative approaches for efficient and sustainable production. This study leverages diverse artificial neural network (ANN) models to assess and predict system efficiency based on key operational variables in membrane reactor systems. The multilayered perceptron (MLP) and radial basis function (RBF) methodologies are employed, with the MLP models optimized across twelve training algorithms and eight activation functions, exploring up to three hidden layers with variable neuron counts. The MLP model, utilizing the Levenberg-Marquard training algorithm and Tangent-Sigmoid activation function, achieved a high correlation coefficient (R2) of 0.9975 for training and 0.9962 for testing, and a mean squared error (MSE) of 0.00425 for training and 0.23951 for testing, indicating precise and accurate efficiency predictions. The Log-Sigmoid activation function also performed well, with R² values of 0.9971 (training) and 0.9961 (testing), and MSE values of 0.004086 (training) and 0.17694 (testing). Optimization of the RBF network identified the best performance with a spread parameter of 1 and 35 neurons, although the MLP model demonstrated superior accuracy and reduced computational time. Statistical analysis, encompassing correlation coefficient, mean squared error, Root Mean Squared error, absolute average deviation, absolute average relative deviation, and runtime, confirms the network’s consistent and accurate estimation of system efficiency across various input variables. The study highlights that applying tansig and logsig activation functions, configured with neuron counts of 20, 17, 6 and 23, 20, 2 at the first, second and third hidden layers, respectively, offers enhanced accuracy and reliability. The MLP model’s high performance underscores its potential to identify optimal conditions for H2 generation based on system efficiency, thereby advancing membrane reactor technology for hydrogen production.

Optimizing macroalgal biochar-based catalysts for monophenols recovery during wood dust catalytic pyrolysis: A comparative study using response surface methodology and artificial neural network

This study aimed to explore the optimization of macroalgal biochar-based catalysts (MBBCs) for enhanced monophenolic production in biomass pyrolysis. By utilizing response surface methodology (RSM) and artificial neural network (ANN), key variables in the activation process during preparation of MBBCs were identified including activation temperature, activator ratio, and CO2 residence time. The results showed that the ANN model, with an R2 of 0.9816, accurately predicted optimal catalyst activation conditions, closely matching the experimentally determined optimal conditions (ENPC800-1:2–30). The bio-oil yield decreased under ENPC800-1:2–30 catalyst, but the monophenolic content increased significantly, reaching a relative content of 60 %, with a total concentration of phenol, cresols, and ethylphenol amounting to 35.54 mg/gbio-oil. The present study revealed significant changes in non-condensable gas composition, including increased hydrogen and carbon monoxide which aid in enhancing their energy applications. Thus, the optimized MBBCs greatly help in improving the bio-oil and non-condensable gas quality and thereby providing vital insights over sustainable chemical production through biomass pyrolysis.

Machine learning based prediction for bulk porosity and static elastic modulus of Yungang Grottoes sandstone

In this work, four mainstream machine learning (ML) techniques are used to evaluate the bulk porosity and static elastic modulus of weathered Yungang Grottoes sandstone. Datasets are gathered from the experiments, which includes 432 groups effective experimental data including 8 inputs features. bulk porosity and static elastic modulus were considered as outputs to determine the weathering degrees of Yungang Grottoes sandstone. The 4 performance criteria were used to evaluate the ML models. Results demonstrate that the Artificial Neural Network (ANN) is the best-fitted models for estimating the bulk porosity and static elastic modulus compared to Multiple Linear Regression (MLR), Support Vector Regression (SVR), Gaussian Process Regression (GPR). The accuracy of the trained model for static elastic modulus is slightly higher than that of bulk porosity. The GPR and ANN model can accurately predict the bulk porosity and static elastic modulus in training stages. The ANN with multi-hidden layers developed is competent with high degree of precision and generalization ability for bulk porosity and static elastic modulus compared to other selected regression-based ML models (MLR, SVR, and GPR). The coefficient of determinations of ANN in the range of (0.9537–0.9641) during the testing stages is more stable and higher than that of (0.8883–0.9453) other built ML models. The prediction efficiency of pretrained ANN model was well adjusted for the actual and forecast datasets at the training and testing stages, and the error range was no more than 0.7% and 0.15 GPa at both stages of prediction for bulk porosity and static elastic modulus respectively. And the ANN based static elastic modulus prediction model’s error proportions significantly decreased and were confined to a modest range between + 10% and − 10%. The proposed surrogate models are valid for the bulk porosity ranging from 7 to 14% and the static elastic modulus ranging from 0.7 to 1.4 Gpa, which can be utilized for the accurate and fast prediction of the weathering degrees of Yungang Grottoes sandstone.

Application of neural network for automatic symbol recognition in production of electronic navigation charts from paper charts

Abstract This research boarded on a novel initiative to replace the error-prone and labour-intensive process of converting Paper Nautical Chart (PNC) symbols to Electronic Navigational Chart (ENC) symbols with a more efficient and automated manner using Artificial Intelligence (AI). The proposed method applies the Convolutional Neural Network and YOLOv5 model to recognise and convert symbols from PNC into their corresponding ENC formats. The model's competence was evaluated with performance metrics including Precision, Recall, Average Precision, and mean Average Precision. Among the different variations of the YOLOv5 models tested, the YOLOv5m version revealed the best performance achieving a mean Average Precision of 0 ⋅ 837 for all features. A confusion matrix was used to visualise the model's classification accuracy for various chart symbols, underlining strengths and identifying areas for improvements. While the model has demonstrated high ability in identifying symbols like ‘Obstruction’ and ‘Major/Minor Lights’, it exhibited lesser accuracy with ‘Visible Wreck’ and ‘Background’ categories. Further, the developed graphical user interface (GUI) allowed users to interact with the artificial neural network model without demanding detailed knowledge of the underlying programming or model architecture.

Hybrid WARIMA-WANN algorithm for data prediction in bridge health monitoring system

Application of data-driven algorithm to model and predict the future trend of the structural health monitoring (SHM) data serves as important references for the future safety evaluation and decision-making of the bridges. An innovative hybrid WARIMA-WANN algorithm combining the Wavelet model, the autoregressive moving average (ARIMA) model and the artificial neural network (ANN) model is proposed to predict massive monitoring data in the bridge SHM system. The WPT serves as the decomposition function for the linear and nonlinear components of the hybrid model. The separated linear/nonlinear components are individually predicted with the ARIMA/ANN models and then combined to obtain the prediction values of the future data. The hybrid method is applied to predict the deflection values of a single-tower cable-stayed bridge. Multiple parameters in individual models are discussed and selected to increase the credibility of the prediction results for the example bridge. The results show that the proposed hybrid algorithm successfully captures the linear and nonlinear coupling relationships inside the SHM data from the frequency perspective of view and shows the best prediction performance when compared with the other four existing models. Different prediction steps obviously influence prediction performance due to the periodic nature of the bridge SHM data.

Presenting a transdisciplinary robust approach for comprehensive assessment of large-scale underground water resources in western Indo-Gangetic Basin

Overexploitation, pollution, and anthropogenic activities threaten the sustainability of groundwater resources in the western Indo-Gangetic Basin. Meanwhile, distinguishing regions prone to contamination and understanding the natural and anthropogenic factors affecting groundwater quality is challenging due to the heterogeneous nature of aquifer systems and the lack of high-resolution spatial and temporal data on aquifer protective hydrogeological layers. This study presents a transdisciplinary robust approach combining regional electrical resistivity surveys, hydrogeological data, physicochemical analyses, and geospatial datasets to identify regions prone to contamination and understand the impacts of natural and anthropogenic factors on groundwater resources. This approach involves three key steps: evaluating the geohydraulic nature of aquifer protective hydrogeological layers, mapping the aquifer vulnerability index (AVI), and conducting comparative analyses of potentially vulnerable areas with groundwater quality index (GWQI) and hydrological factors. Firstly, model-based inversion of ID Vertical Electrical Sounding (VES) data provides insights into geoelectrical indices such as depth, thickness, apparent resistivity, longitudinal conductance, transverse resistance, and longitudinal resistivity of aquifer protective hydrogeological layers. Second, the Artificial Neural Network (ANN) model is used as a multilayer perceptron network to simulate hydraulic conductivity (K) using geoelectrical indices of aquifer protective hydrogeological layers. Subsequently, by considering ANN-derived K and VES-derived h of aquifer protective hydrogeological layers, the dynamic hydraulic resistance to the vertical flow of wastewater through the protective hydrogeological layers evaluated the index of the potentially vulnerable areas. Comparative analyses of potentially vulnerable areas with GWQI and hydrological factors (e.g., digital elevation model, soil, drainage density, lineament density, slope) enhance understanding regions prone to contaminants and land surface stress. Findings show that the ANN approach to simulate K, reducing effort with costs associated with slug testing is significant for AVI assessment. Furthermore, the geohydraulic characteristics, vulnerability indexing, and comparative analyses assist in identifying contamination-prone areas, improving groundwater resource protection and exploration activities.

Examining the personal growth of college teacher educators through the lens of human development ecology: An approach utilizing artificial neural networks (ANNs) modeling

College teacher educators play a crucial role in improving teaching quality and have a significant impact on educational development. They must engage in a process of self-growth to achieve personal development, considering the interaction between individuals and their environment. This process involves deep exploration of their knowledge, skills, and attitudes, as well as an understanding of the broader institutional and cultural contexts. Through self-reflection and continuous learning, college teacher educators can foster personal growth by acquiring new teaching strategies, developing effective communication and interpersonal skills, and cultivating a reflective and adaptive teaching practice. Collaboration with colleagues, engagement with professional communities, and involvement in educational research and innovation are also essential aspects of personal growth. Creating supportive environments that facilitate the continuous development of college teacher educators is crucial for enhanced teaching quality and educational advancement. In this study, an artificial neural network (ANN) was employed to estimate the impact of changes in knowledge and skill development and reflection and continuous learning on the acquisition of new educational strategies, development of communication and interpersonal skills, and reflective and adaptive practice. The ANN's predictions indicate that increasing reflection and continuous learning has a more positive impact on acquiring new educational strategies compared to increasing knowledge and skill development alone. Additionally, when both knowledge and skill development and reflection and continuous learning are combined, it results in substantial growth in the development of communication and interpersonal skills and reflective and adaptive practice. The accuracy of the neural network's predictions was evaluated using linear regression, showing an acceptable level of error when compared to the experimental results.