Artificial Neural Network Model For Prediction Research Articles

Comparative Ergonomic Posture Analysis of CNC Milling Machine Workers through Digital Human Modeling and Artificial Neural Networks

Objectives: To analyze the critical postures of the CNC milling machine operators by RULA (Rapid Upper Limb Assessment) scores and develop an ANN (Artificial Neural Network) prediction model. Methods: The methodology includes a postural analysis of 40 male CNC milling machine operators across Bangladesh, employing both manual (using manual RULA assessment worksheet) and digital (using CATIA V5R21 software) RULA methods complemented by an ANN prediction model. Finally, Digital RULA scores through DHM (Digital Human Modeling) and ANN predicted RULA scores would be compared. Findings: Digital RULA analysis reveals that lifting, carrying, and positioning are the most crucial ergonomic postures, and the most prominent high-risk category limbs are wrist and arm. The overall initial RULA score for lifting, carrying, and positioning are 7, 6, and 7, respectively, and reduced to 3, 3 and 4 respectively for ergonomically designed posture. The ANN model, structured with input, hidden, and output layers of 7, 10, and 1 nodes, significantly refines ergonomic risk prediction by aligning predicted scores closely with actual outcomes during the first stage, emphasized for training. It demonstrates a perfect correlation (R=1) in training, testing, validation, and overall performance for using manual RULA scores. The model's accuracy is further evidenced by minimal prediction offsets across all datasets for digital RULA score in the second stage, with correlation coefficients of 0.87003 (training), 0.93676 (validation), 0.89113 (testing), and (0.88395) for overall. This study contributes significant advancements in ergonomic risk assessment, highlighting the adoption of improved postures to reduce musculoskeletal disorders. Novelty: Employing both manual and DHM methods for RULA score calculation combined with ANN model, which can predict postural risk as floating number and fit a wider range of parameters. Keywords: ANN, CNC, Digital Human Modeling (DHM), Ergonomics, RULA

Impact of Real-Time Blind Slat Angle Control on Reducing Total Energy Consumption of a Library Building Using an Optimized Artificial Neural Network Model: A Case Study of the Six Moroccan Thermal Zones

Abstract The prevalence of extensive glazed areas in contemporary buildings contributes significantly to solar radiation infiltration, elevating energy demands and causing discomfort for occupants. Window shading devices play a pivotal role in addressing this challenge. This paper presents the development and optimization of an artificial neural network (ANN) predictive model, designed to enable real-time control of slat angles by predicting total energy loads, specifically during summer (for cooling and lighting purposes). The refined model demonstrates high precision, achieving a normalized root mean square error (nRMSE) of approximately 1.72% and a correlation coefficient (R) of around 0.999, despite utilizing limited meteorological data. Key inputs for the model include solar radiation, solar altitude, and external temperature, with a particular focus on slat reflectivity. The study assesses the efficiency of three slat types based on their reflectivity: high (80%), medium (50%), and low (20%). Additionally, the research explores the impact of window-to-wall ratio (WWR) values on the control system's efficacy, revealing a positive correlation between higher WWR values and improved energy savings through ANN slat angle control. Furthermore, the study extends the applicability of the ANN model to the six thermal zones in Morocco, affirming its generalization across diverse environmental conditions.

Unlocking waste potential: A neural network approach to forecasting sustainable acetaldehyde production from ethanol upcycling in biomass waste gasification

The upcycling of biomass waste gasification process for ethanol and acetaldehyde production has been augmented by integrating secondary and tertiary processes. A simulation model using Aspen Plus, developed from experimental investigations to assess the viability of this proposed system. Furthermore, this model has been utilized to create an Artificial Neural Network (ANN) prediction model. Process sustainability analysis demonstrated an energy efficiency of 64 % while economic viability up to 80 % process efficiency with an Internal Rate of Return (IRR) of 6 % and a payback period of 2107 days. An optimization strategy and an artificial neural network (ANN)-based predictive model have been developed. Optimization results revealed that a gasifier temperature of around 600 °C, a gasifying agent ratio of 2.0, and an acetaldehyde reactor temperature of 300 °C yield better process outcomes and revenue. The ANN model exhibited robust performance with coefficient of determination (R2) values ranging from 0.92 to 0.98 for acetaldehyde, hydrogen, and total revenue. Mean absolute error (MAE) and mean absolute percentage error (MAPE) fell within the range of 0.03–0.1 and 0.1–1.12 %, respectively. Consequently, given the favorable sustainability and predictive model performance, this study can be used for similar endeavors.

Predicting Bulk Density for Agglomerated Raspberry Ketone via Integrating Morphological and Size Metrics Using Artificial Neural Networks

The bulk density of the particles, which is directly related to transportation and storage costs, is an important basic characteristic of products as well as an important parameter in many processing systems. This work quantified the relationship between the tapped bulk density of raspberry ketone with different degrees of agglomeration and morphological metrics (particle shape descriptors and roughness descriptors) and size metrics (size descriptors) and developed an artificial neural network (ANN) prediction model for the tapped bulk density of raspberry ketone. Samples prepared under different conditions were sieved and remixed, the tapped bulk density of the particles was then measured, and the descriptor features of the particles were obtained by combining them with image processing. The dimensions of the variables were decreased by principal component analysis and variance processing. To overcome the hyperparameter estimation of the heuristic-based artificial neural networks, the network model architectures were optimized by a neural architecture search strategy combining two-objective optimization. The results demonstrated that the tapped bulk density of raspberry ketone products is not only related to the descriptors of particle size and shape but also has a non-negligible relationship with particle roughness descriptors. The performance of the optimal ANN model demonstrated that the model can well predict the tapped bulk density of raspberry ketone with different degrees of agglomeration. The ANN model obtained by extracting morphology and size metrics through online image analysis can be used to measure the tapped bulk density in real-time and has the potential to be used for developing model-based online process monitoring.

Neural network assisted for parameters uncertainty of isogrid tube under different load types

Isogrid structures offer an excellent balance between mass and rigidity, making them a crucial component in various industrial sectors. However, like all types of structures, isogrids are susceptible to performance degradation due to uncertainties in geometrical and material parameters arising from manufacturing errors and exposure to critical service environments. This paper addresses the effects of parameter uncertainty on the structural behavior of isogrid tube structures. To this end, a Sobol index is employed to assess the sensitivity of the tube’s natural frequency, axial buckling, torsional buckling, and overall mass to three geometrical and one material parameter, each subject to a 10% random variation. The data for this analysis were generated using a parametrized numerical model specifically developed for this purpose. To optimize computational costs further, an artificial neural network predictive model is proposed, which accurately replicates the behavior of the numerical model. The results indicate that the angle between helicoidal ribs is the most influential parameter.

Enhancing municipal solid waste leachate treatment efficiency: AI-based prediction of electrocoagulation/flocculation recovery using iron electrodes

ABSTRACT This study addresses a gap in municipal leachate (MUPL) treatment by introducing a pioneering application of artificial intelligence (AI) in the electrocoagulation/electroflocculation (EC/EF) process utilizing iron electrodes. The overarching aim is to demonstrate the efficacy of AI, particularly a multi-layer perceptron (MLP)-based feed-forward artificial neural network (ANN) incorporating the Levenberg-Marquardt (LMb) algorithm, in predicting and optimizing EC/EF outcomes for turbidity (TDY) removal. The research methodology involved experimentation and robust ANN data modeling. The significance of this work emerges from the successful integration of AI, showcasing its potential in advancing wastewater, demonstrated through a strong positive correlation (0.994) between the ANN model predictions and experimental outcomes. The study achieves a remarkable 99.4% TDY removal at an electrolysis time of 10 min and contributes valuable insights into the critical parameters influencing the EC/EF process. Results from the ANN modeling exhibit high predictive accuracy, supported by elevated R-squared values and minimal mean square error. Statistical analyses underscore the significance of key process parameters, highlighting the influential roles of current intensity and settling time. The study emphasized the favourable impact of maintaining an acidic pH range, as it reduced electrostatic repulsion between particles, facilitating pollutant agglomeration, and identified electrolysis time as a key factor in enhancing treatment efficiency, supported by a strong positive correlation between electrolysis time and TDY reduction. Energy cost savings were realized by not requiring temperature elevation. Achieving a 99.4% TDY removal translates to substantial reductions in other pollutants present in the MUPL, thereby elevating water quality and ensuring compliance.

Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature.

Male infertility is a relevant public health problem, but there is no systematic review of the different machine learning (ML) models and their accuracy so far. The present review aims to comprehensively investigate the use of ML algorithms in predicting male infertility, thus reporting the accuracy of the used models in the prediction of male infertility as a primary outcome. Particular attention will be paid to the use of artificial neural networks (ANNs). A comprehensive literature search was conducted in PubMed, Scopus, and Science Direct between 15 July and 23 October 2023, conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We performed a quality assessment of the included studies using the recommended tools suggested for the type of study design adopted. We also made a screening of the Risk of Bias (RoB) associated with the included studies. Thus, 43 relevant publications were included in this review, for a total of 40 different ML models detected. The studies included reported a good quality, even if RoB was not always good for all the types of studies. The included studies reported a median accuracy of 88% in predicting male infertility using ML models. We found only seven studies using ANN models for male infertility prediction, reporting a median accuracy of 84%.

Application of ANN for coastal water quality prediction: Quang Binh case study

The coastal areas of Quang Binh province play a crucial role not only in the economic and tourism development of the coastal province but also in the overall development of the Northern Central region. Therefore, monitoring and forecasting seawater quality in this region is vital. However, the water quality condition assessment faces many limitations due to the lack of measured data and the complication of numerical models. Meanwhile, the artificial intelligence model for simulating and predicting water quality has been widely applied due to its timely and reliable calculating abilities. This research has piloted the prediction of some water pollution parameters on the coast of Quang Binh province using an artificial neural network (ANN) model. This presents a novel approach to identifying implicit relationships between variables based on data analysis techniques via ANN. An ANN model was built to analyze the measured environmental time series data at Dong Hoi station, Quang Binh province, from 2002 to 2022. The calculation results of the training (70% of the data set, NSE: 0.81) and testing (the rest 30% of the data set, NSE: 0.5) of the model have satisfied the total coliform parameter, indicating the promise of applying the ANN model for water quality prediction.

Optimizing high-speed rotating shaft vibration control: Experimental investigation of squeeze film dampers and a comparative analysis using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM)

This research paper presents a comprehensive experimental and statistical approach for the analysis of vibration amplitudes in a high-speed rotating shaft employing a squeeze film damper (SFD). The research combines a comprehensive analysis that connects input parameters and response parameters, with a special emphasis on vibration amplitudes along the X and Z axes. This research utilizes the rigorous procedures of Response Surface Methodology (RSM) together with a Box-Behnken design and harnesses the capabilities of Artificial Neural Network (ANN) optimization techniques. The variables under scrutiny encompass critical factors such as shaft rotational speed, extending up to 8000 rpm, oil pressure, with a range extending up to 100 bar, and various oil mix ratios, spanning from 10 % to 50 %. Various statistical measures are computed to assess the errors and coefficients of determination of the projected models. The artificial neural network (ANN) model has shown somewhat reduced prediction errors and a greater coefficient of determination compared to the Response Surface Methodology (RSM) for both the x and z axes of vibration amplitude. The values of mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R-squared) are found for both x and z axes from RSM (3.50, 3.77), (4.50, 4.72), and (0.81, 0.79), (1.88, 1.70), (2.41, 2.12), and (0.94, 0.95), respectively, using the ANN model. The overall mean absolute percentage error (MAPE) from the ANN model of both the x and z axes (9.73 %, 9.38 %) is found to be lower compared to the RSM model (18.18 %–20.50 %). The conclusive research reveals that the artificial neural network (ANN) prediction model outperforms the regression model based on Response Surface Methodology (RSM), exhibiting superior accuracy in predicting vibration amplitudes. The ANN approach is an excellent option for calculating vibration amplitudes in high-speed rotating shafts. Additionally, it provides significant benefits in terms of effectiveness and time economy. This research provides valuable new insights into the most efficient modeling approaches for vibration management and highlights the benefits of using Artificial Neural Networks (ANN) for predictive assessments in this context. Particle Swarm Optimization is used to minimize the vibration amplitude of the shaft along the x and z axes using experimental data.

Artificial Neural Network Prediction Model for Maternal Health Services Quality in Nigeria

This study presents an innovative approach in predicting the quality of maternal health services in Nigeria using artificial neural networks (ANNs). It focuses on a binary classification task in categorizing maternal health services facilities into "High_Quality" and "Low_Quality" classes. Dataset was collected from diverse healthcare facilities in Nigeria through a WHO Health Resources and Services Availability Monitoring System (HeRAMS) cross sectional-survey report. The collected data was preprocessed; the ANN architecture was developed and trained using back-propagation method. The network was modeled using feed-forward modeling approach with 16 variables at the input layer, 2 hidden layers of 65 and 55 neurons each, and an output of 2 neurons. Hence, 87.17% accuracy with only 2.48% deviation at the 50 epoch was achieved. The model's performance was evaluated using standard metrics of accuracy (87%), precision (88%), recall (89%), and F1-score (88%) to assess its ability to accurately classify maternal health services facilities. The findings of the study can have significant implications to policymakers, healthcare providers, and maternal health advocates to optimize healthcare resources and achieve better maternal health outcomes in Nigeria. Data availability, potential biases in the data, and the subjective nature of some quality-related indicators were acknowledged as limitations. However, despite these challenges, the ANN classification model remains a valuable tool for supporting decision-making in maternal healthcare facility delivery service in Nigeria. Artificial neural network, Binary classification, Health services, Maternal health, Predictive modeling

Machine intelligence-accelerated discovery of all-natural plastic substitutes.

One possible solution against the accumulation of petrochemical plastics in natural environments is to develop biodegradable plastic substitutes using natural components. However, discovering all-natural alternatives that meet specific properties, such as optical transparency, fire retardancy and mechanical resilience, which have made petrochemical plastics successful, remains challenging. Current approaches still rely on iterative optimization experiments. Here we show an integrated workflow that combines robotics and machine learning to accelerate the discovery of all-natural plastic substitutes with programmable optical, thermal and mechanical properties. First, an automated pipetting robot is commanded to prepare 286 nanocomposite films with various properties to train a support-vector machine classifier. Next, through 14 active learning loops with data augmentation, 135 all-natural nanocomposites are fabricated stagewise, establishing an artificial neural network prediction model. We demonstrate that the prediction model can conduct a two-way design task: (1) predicting the physicochemical properties of an all-natural nanocomposite from its composition and (2) automating the inverse design of biodegradable plastic substitutes that fulfils various user-specific requirements. By harnessing the model's prediction capabilities, we prepare several all-natural substitutes, that could replace non-biodegradable counterparts as exhibiting analogous properties. Our methodology integrates robot-assisted experiments, machine intelligence and simulation tools to accelerate the discovery and design of eco-friendly plastic substitutes starting from building blocks taken from the generally-recognized-as-safe database.

Pyrolysis of macadamia nut peel using multicomponent Gaussian kinetic modeling and ANN analysis

The physicochemical properties of macadamia nut peel (MNP) were explored based on pyrolysis characteristics, kinetics, thermodynamic parameters, reaction mechanism, and artificial neural network (ANN) application to evaluate its suitability and potential for pyrolytic conversion into bioenergy products. Pyrolysis experiments of MNP were performed using a thermogravimetric analyzer in a nitrogen atmosphere. The thermal degradation process of MNP can be successfully modeled into pseudo hemicellulose (PS–H1), cellulose (PS–C2), and lignin (PS-L3) using a multicomponent Gaussian kinetic model with high correlation coefficient (R2 > 0.99). The average apparent activation energy was 179.28 kJ/mol for PS-H1, 192.31 kJ/mol for PS-C2, and 129.42 kJ/mol for PS-L3 using Ozawa-Flynn-Wall, Kissinger-Akahira-Sunose, Starink, and Tang four model-free methods. Master plots method was used to determine the reaction mechanism models of three pseudo components, and the pyrolysis progress can be described well using order-based and nucleation mechanism models. The thermal degradation behaviors of MNP were predicted using artificial neural network, and the best artificial neural network prediction model was ANN (3*15*1). Based on the physicochemical properties, pyrolysis characteristics, kinetics, thermodynamic parameters, reaction mechanism and ANN prediction model, MNP is suitable for pyrolytic conversion into bioenergy products.

ANN prediction model of final construction cost at an early stage

ABSTRACT Previous studies developed models to predict final construction cost (FCC) values based on many inputs, which makes them difficult to use. However, relying on models with relatively few inputs will reduce the accuracy of the prediction results. This paper aims to develop an artificial neural network (ANN) model to predict the FCC based on contract cost (CC), contract duration, and project sector at an early stage of a project. The data collected and used for the ANN model was 135 Saudi Arabian construction projects. The Zavadskas and Turskis logarithmic approaches, and the Pasini method were utilized to overcome the limited data. Then, the ANN model was developed through two stages. The purpose of the first stage was to enhance the data by identifying the abnormal data using absolute percentage errors (APE). The enhanced data was used to develop the ANN in the second stage. The finding showed that the ANN model provided an average MAPE (mean absolute percentage error) of 18.7%. The MAPE of the ANN model is decreased to 8.7% on average by deleting data with an APE higher than 35%. The model allows stakeholders to evaluate the financial importance of potential risks and develop appropriate risk management strategies.

Taguchi method and neural network for efficient β‐ketoenamine synthesis in deionized water

AbstractThe optimization of performance parameters, in particular the yield of the synthesis reaction of β‐enaminones in demineralized water, is crucial to improve their efficiency and accuracy. In this report, we investigate the optimization of the synthesis of β‐ketoenamines in deionized water by controlling several parameters such as reaction time, temperature, amine equivalent, acid percentage, and stirring rate. An orthogonal L16 (45) network was created using Taguchi's approach, allowing for the best possible parameters. To forecast the contribution of each parameter, analysis of variance (ANOVA) techniques are also used. Multiple linear and nonlinear regression (MLR, MNLR) and multilayer perception artificial neural network (MLP‐ANN) predictive models were developed. Analysis of the results led to optimized design parameters, with time = 6 h, temperature = 25°C, amine equivalent = 1.5, acid percentage = 20%, and stirring rate = 1000 rpm, leading to a maximum yield of 63%. ANOVA analysis revealed that temperature, stirring rate, acid percentage, and time are the parameters with the greatest influence. The least sensitive parameter is the amine equivalent. The two main interactions are temperature * acid % and amine equivalent * rpm. The MLP‐ANN predictions are in good agreement with the experimental values, resulting in a higher R2 compared to the quadratic regression model and the MLR model. By using molecular docking studies, the produced compounds' biological activity was investigated. Some of the synthesized compounds appear to be interesting and could be used for therapeutic applications. The results of this study give us insight into the gentle, cost‐effective, and biologically active synthesis of β‐enaminones in deionized water.

Establishment and analysis of artificial neural network diagnosis model for coagulation-related molecular subgroups in coronary artery disease.

Background: Coronary artery disease (CAD) is the most common type of cardiovascular disease and cause significant morbidity and mortality. Abnormal coagulation cascade is one of the high-risk factors in CAD patients, but the molecular mechanism of coagulation in CAD is still limited. Methods: We clustered and categorized 352 CAD paitents based on the expression patterns of coagulation-related genes (CRGs), and then we explored the molecular and immunological variations across the subgroups to reveal the underlying biological characteristics of CAD patients. The feature genes between CRG-subgroups were further identified using a random forest model (RF) and least absolute shrinkage and selection operator (LASSO) regression, and an artificial neural network prediction model was constructed. Results: CAD patients could be divided into the C1 and C2 CRG-subgroups, with the C1 subgroup highly enriched in immune-related signaling pathways. The differential expressed genes between the two CRG-subgroups (DE-CRGs) were primarily enriched in signaling pathways connected to signal transduction and energy metabolism. Subsequently, 10 feature DE-CRGs were identified by RF and LASSO. We constructed a novel artificial neural network model using these 10 genes and evaluated and validated its diagnostic performance on a public dataset. Conclusion: Diverse molecular subgroups of CAD patients may each have a unique gene expression pattern. We may identify subgroups using a few feature genes, providing a theoretical basis for the precise treatment of CAD patients with different molecular subgroups.

Development of single point prediction model using artificial neural network and experimental validation for pump as turbine applications

This paper proposed a new approach based on artificial neural networks (ANNs) to select the most appropriate centrifugal pumps for reverse operation i.e. pump as turbines (PAT), by predicting parameters at Best Efficiency Point from pump mode data. Both, exhaustive experimental data of 49 pumps (specific speeds 9−104) collected from open literature and by in-house experimentation data, considered input data-set as pump and target data-set as PAT are used for training the ANN models with two different training functions: Levenberg–Marquardt and Bayesian regularisation. The proposed ANN prediction model shows a deviation lower than 10% compared to the respective experimental value. Additionally, the trained ANN model tested with four pumps (not included in training data sets of proposed models) shows maximum absolute deviation in head number, flow number, and efficiency within the 10%, 7%, and 6% range, respectively, compared to the experimental values. Furthermore, the efficiency of reverse mode evaluated for variation in rotational speed shows a maximum of 3.3% (within 5%) absolute deviation compared to respective experimental results. Overall, the ANNs based prediction model of PAT parameters is recommended compared to conventional models available in the literature as it gives superior results (less than 10% deviation) for practical application.

Uncertainty quantification of unconfined spill fire data by coupling Monte Carlo and artificial neural networks

ABSTRACT Due to the complexity of spill fire, predicting heat release rate (HRR) is a challenging aspect, therefore, identifying key contributors to uncertainty is essential to develop reliable models for fire risk assessment. We propose a framework that couples Artificial-Neural-Network (ANN) and Monte Carlo Uncertainty Quantification (UQ) to quantify the uncertainties of spill fire parameters on peak HRR variance. The ANN spill fire model, trained on experimental spill fire data, shows good agreement with measured HRR values. By coupling with Dakota for Monte Carlo uncertainty propagation, the framework identifies major contributors to peak HRR uncertainty for fixed and continuous unconfined spill fires. This is achieved by investigating two uncertainty sources: data scarcity and input parameters. UQ results indicate that the confidence intervals widen at points where data are scarcer. Sensitivity analysis shows that in the case of fixed quantity spills, the fuel amount and properties parameters contribute to 54.4% and 22.6% of peak HRR variance, respectively. For continuous spills, the discharge rate and fuel properties parameters account for 41.8% and 39.3% of peak HRR variance, respectively. This work can be utilized in developing more advanced predictive ANN models for spill fire scenarios, ultimately enhancing fire probabilistic safety analysis in nuclear power plants.

Artificial neural network and multiple linear regression modeling for predicting thermal transmittance of plain-woven cotton fabric

The present research compares a machine learning model with a statistical model, with specific emphasis on artificial neural networks and multiple linear regression models. The aim of this study is to forecast the thermal transmittance of a plain-woven cotton fabric using input data such as thread density measured in ends per inch, picks per inch, and fabric thickness. The artificial neural network is built using a network with feed-forward backpropagation, and the MATLAB software’s training function trainlm is used to modify its weight and basic values based on Levenberg–Marquardt optimization techniques. The sigmoid transfer function is used to set the layer output and measure network performance in terms of the root mean squared error, mean absolute error percentage, and coefficient of determination which were determined. For the artificial neural network prediction model, the root mean squared error and mean absolute error percentage were 1.05 and 3.132%, respectively, while the coefficient of determination was 0.9307. In contrast, the multiple linear regression prediction model had root mean squared error and mean absolute error percentage values of 2.98 and 8.97%, respectively, along with a coefficient of determination of 0.4727. The results reveal that the artificial neural network model outperforms the multiple linear regression model, showing superior accuracy and robustness in capturing the intricate interactions between important fabric parameters (ends per inch, picks per inch, and thickness) and thermal transmittance values. This research emphasizes the efficiency of artificial neural network modeling as a superior tool for forecasting thermal transmittance in textile applications rather than employing the time-consuming trial-and-error process for delivering significant insights for material engineering and energy-efficient design.

The Degradation of Sulfamethoxazole via the Fe2+/Ultraviolet/Sodium Percarbonate Advanced Oxidation Process: Performance, Mechanism, and Back-Propagate–Artificial Neural Network Prediction Model

The degradation of sulfamethoxazole (SMX) via the Fe2+/Ultraviolet (UV)/sodium percarbonate (SPC) system was comprehensively investigated in this study, including the performance optimization, degradation mechanism, and predicting models. The degradation condition of SMX was optimized, and it was found that appropriate amounts of CFe2+ (10~30 μM) and CSPC (10 μM) under an acidic condition (pH = 4~6) were in favor of a higher degradation rate. According to probe compound experiments, it was considerable that ∙OH and ∙CO3− was the primary and subordinate free radical in SMX degradation, and k∙OH,SMX maintained two times more than that of k∙CO3−,SMX, especially under acidic conditions. The UV direct photolysis and other active intermediates were also responsible for the SMX degradation. These active intermediates were produced via the Fe2+/UV/SPC system, involving ∙HO2, HCO4−, ∙O2 −, or 1O2. Furthermore, when typical anions co-existed, the degradation of SMX was negatively influenced, owing to HCO3− and CO32− possibly consuming ∙OH or H2O2 to compete with SMX. In addition, the prediction model was successfully established via the back-propagate artificial neural network (BP-ANN) method. The degradation rate of SMX was well forecasted via the Back-Propagate–Artificial Neural Network (BP-ANN) model, which was expressed as Ypre=tanh(tanh(xiWih)Who). The BP-ANN model reflected the relative importance of influence factors well, which was pH > t > CFe2+≈CSPC. Compared to the response surface method Box–Behnken design (RSM-BBD) model (R2 = 0.9765, relative error = 3.08%), the BP-ANN model showed higher prediction accuracy (R2 = 0.9971) and lower error (1.17%) in SMX degradation via the Fe2+/UV/SPC system. These findings help us to understand, in-depth, the degradation mechanism of SMX; meanwhile, they are conducive to promoting the development of the Fe2+/UV/SPC system in SMX degradation, especially in some practical engineering cases.

Study on the impact of physical characteristics of soil-rock composite medium on its relative permittivity based on laboratory experiments

The permittivity of soil-rock composite media is crucial for interpreting data originating from geophysical surveys such as ground-penetrating radar (GPR) imaging. At present, relevant theoretical research on the correlation analysis between the electrical and physical parameters of soil-rock composite media has not yet resulted in a complete reference system. This makes it difficult to characterize soil-rock composite media using the electromagnetic characteristic parameters of rock and soil media. In this study, the indoor electromagnetic testing method was used to extract electrical parameters such as the relative permittivity of soil-rock composite, and the influence of water content, density, soil-rock ratio, and other factors on the electrical parameters of soil-rock composite medium was analyzed. Furthermore, an artificial neural network prediction model was established, and the accuracy of the model was evaluated. The results showed that the relative permittivity tends to decrease with an increase in saturation when the moisture content of the mixture is high. The rock has a lower permittivity value than the surrounding soil particles. When the rock content increases, the average permittivity and conductive permittivity of the composite medium decreases. The main factors affecting the electrical properties of soil-rock composite media (in order of their influence, from most to least) are water content, compaction degree, and soil-rock ratio. These results provide a reliable reference for field geophysical exploration.