Levenberg-Marquardt Backpropagation Algorithm Research Articles

Assessing inflation and greenhouse gas emissions interplay via neural network analysis: a comparative study of energy use in the USA, EU, and China

This study examines the relationship between inflation and greenhouse gas (GHG) emissions in three major economies: the United States of America (USA), the European Union (EU), and China. The analysis spans from 1960 to 2021 for the USA and EU, and from 1971 to 2021 for China. A feedforward neural network model, optimized using the Levenberg–Marquardt backpropagation algorithm, was employed to predict GHG emissions based on annual inflation rates and fossil fuel energy consumption. The study integrates historical data on inflation trends with GHG emissions, measured in CO2 equivalents, and fossil fuel energy consumption, expressed as a percentage of total energy use. This multidimensional approach allows for a nuanced understanding of the economic-environmental interplay in these regions. Key findings indicate a nonlinear response of GHG emissions to inflation rates. In the USA, GHG emissions begin to decrease when inflation rates exceed 4.7%. Similarly, in the EU, a steep reduction in emissions is observed beyond a 7.5% inflation rate. China presents a more complex pattern, with two critical inflection points: the first at a 4.5% inflation rate, where GHG emissions start to decline sharply, and the second at a 7% inflation rate, beyond which further increases in inflation do not significantly reduce emissions. A critical global insight is the identification of a uniform inflation rate, around 4.4%, across all regions, at which GHG emissions consistently increase by 1%, hinting at a shared global economic behavior impacting the environment. This discovery is vital for policymakers, emphasizing the need for tailored regional strategies that consider unique economic structures, energy policies, and environmental regulations, alongside a coordinated global approach.

Mechanical characteristics of waste-printed circuit board-reinforced concrete with silica fume and prediction modelling using ANN.

The use of electronic waste in cement concrete as a fibre additive has proven to be very promising for improving mechanical characteristics and developing sustainable construction materials to reduce the waste dumped in landfills. The following study investigated the effect of electronic waste (printed circuit boards (PCBs)) on the mechanical properties of concrete and predicted the same properties with an appropriate machine learning technique. PCB fibres 45mm in length and 1.5mm in width were manufactured and added as fibre additions to two sets of concrete mixes with and without silica fume. A 10% volume replacement of cement was substituted with silica fume (SF) to enhance the characteristics of PCB fibre-reinforced concrete and minimize cement consumption. The study included an evaluation of the fresh properties and mechanical characteristics after a 28-day curing period; thereafter, the results were compared and studied using the Levenberg-Marquardt backpropagation algorithm for predictions. The results show that the mechanical properties improved up to a 5% addition of PCB fibres, resulting in strengths of 63.55MPa and 69.92MPa for mixtures of PCB5% and SFPCB5%, respectively. A similar trend was achieved for other properties, such as the tensile and flexural strengths. The results of the ANN model predicted values with R2 values ranging from 0.94 to 0.99, indicating the efficacy of the model.

Evaluation of shale oil and gas plays - Part I: Shale reservoir property modelling of the North Sea Kimmeridge Clay Formation

The Kimmeridge Clay Formation (KCF) forms the source rock for most conventional hydrocarbon accumulations within the UK North Sea. However, only a few previous studies have analyzed the potential of the KCF for unconventional resources (i.e., shale oil and gas).Here, we use machine learning techniques combined with established rock property equations to generate geochemical, petrophysical and geomechanical logs for 16 wells within Quadrant 15 of the Outer Moray Firth, UK North Sea. The neural network models used to generate geochemical logs are trained using the Levenberg-Marquardt backpropagation algorithm. The generated well logs and new KCF depth maps, constrained by an existing Base Cretaceous Unconformity (BCU) map of the UK North Sea and well top information from 48 wells, are used for 3D geostatistical modelling of KCF properties across the area. The resulting KCF property maps allow us to assess the shale oil and gas play potential across the region.Our results suggest good organic richness and hydrocarbon yield potential for the KCF within the study area, with up to 9 wt% original total organic carbon (TOCo), 48 mg/g original hydrocarbon yield (S2o), and 607 mg/gTOC original hydrogen index (HIo). Modelled total porosity values range between 2 and 13%, and brittleness indices lie between 25 and 65%, within the range reported for proven shale oil and gas plays.A sweet spot map, created by integrating our modelled KCF properties, indicates prospective areas for shale oil and gas exploitation within the central Witch Ground Graben, and to a lesser extent areas of the Piper Shelf and Claymore-Tartan Ridge. These areas show good potential based on all investigated properties, with respect to industry standards.

Insights into levofloxacin adsorption with machine learning models using nano-composite hydrochars

Hydrothermal carbonization was applied to taro peel wastes to produce hydrochars using a facile and environmentally friendly process. Four different entities were prepared: hydrochar (TPh), phosphoric-activated hydrochar (P-TPh), and silver@hydrochars (Ag@TPh, Ag@P-TPh). The elemental compositions of the single and composite hydrochars were confirmed by EDX. Among the produced hydrochars, the morphology of the Ag@hydrochar composites demonstrated more wrinkled structure, and Ag nanoparticles decorated the surface. The optimal experimental conditions for levofloxacin adsorption were determined to be a contact time of 45 min, hydrochar dose of 0.15 g L−1, and pH of 7. The best adsorption performances were assigned to Ag@hydrochars. Two machine learning models were applied to predict the levofloxacin adsorption efficiency of the Ag@hydrochars. A central composite design (CCD) and a 3-10-1 artificial neural network (ANN) model were developed to estimate the removal performance of levofloxacin using Levenberg-Marquardt backpropagation algorithm based on correlation and error analysis of the adopted training functions. Furthermore, the ANN sensitivity analysis revealed the order of the relative importance variable as initial concentration> hydrochar dose> pH. The predicted values of the CCD and ANN models fitted the experimental results with R2> 0.989. Therefore, the applied models were effective in predicting levofloxacin removal under different operating conditions. This work provides an open option for the sustainable management of food industry wastes and the possibility of waste valorization to effective hydrochar composites to be applied in water treatment processes.

The significance of radiative heat and mass transfer through a vertical sheet with chemical reaction: Designing by artificial approach Levenberg-Marquardt

The present study examines the impact of incorporating soft computing algorithms during neural network training on the evaluation and prediction performance of artificial neural networks. The research centers on a magneto hydrodynamic flow model (RHMT-VSCR) that depicts the movement of rotating fluid along a vertical sheet in a permeable medium. Furthermore, the model considers the impact of heat source-sink interactions, thermal radiation, and reactive species in conjunction with the effects of Hall current on the enhancement of energy and solute profiles. Because the induced magnetic field has a negligible magnetic Reynolds number, it is disregarded. A set of governing nonlinear PDEs is converted to a system of ODEs by applying an appropriate postulate of similarity variables in order to analyze the system. The multilayer perceptron network utilizes a supervised Levenberg–Marquardt Backpropagation algorithm (LMLA-BPNN) to ascertain the ideal quantity of neurons to be included in the hidden layer of artificial neural networks intended for modeling purposes. The bvp4c numerical approach is utilized to establish a continuous neural network mapping, which produces datasets that are utilized for the purposes of authentication, training, and testing. The range i.e., 10−2−10−8 of absolute error of the reference and target data demonstrates the optimal accuracy performance of LMLA-BPNN networks. After acquiring knowledge of neural network mapping, it is applied to approximate solutions for a wide range of scenarios through the manipulation of physical constraints, including suction/injection quantities, magnetic parameter, and porosity parameter, which influence the characteristics of flow, energy, and concentration.To assess the precision of the proposed method, statistical graphs based on regression, mean squared error analysis graphs, and error histogram graphs are employed. The results of the research demonstrate that the Levenberg-Marquardt Backpropagation neural network mappings' derivation, convergence, authentication, and stability were effectively validated.

Intelligent computing for electromagnetohydrodynamic bioconvection flow of micropolar nanofluid with thermal radiation and stratification: Levenberg–Marquardt backpropagation algorithm

The Levenberg–Marquardt (LM) backpropagation optimization algorithm, an artificial neural network algorithm, is used in this study to perform integrated numerical computing to evaluate the electromagnetohydrodynamic bioconvection flow of micropolar nanofluid with thermal radiation and stratification. The model is then reduced to a collection of boundary value problems, which are solved with the help of a numerical technique and the proposed scheme, i.e., the LM algorithm, which is an iterative approach to determine the minimum of a nonlinear function defined as the sum of squares. As a blend of the steepest descent and the Gauss–Newton method, it has become a typical approach for nonlinear least-squares problems. Furthermore, the stability and consistency of the algorithm are ensured. For validation purposes, the results are also compared with those of previous research and the MATLAB bvp4c solver. Neural networking is also utilized for velocity, temperature, and concentration profile mapping from input to output. These findings demonstrate the accuracy of forecasts and optimizations produced by artificial neural networks. The performance of the bvp4c solver, which is used to reduce the mean square error, is used to generalize a dataset. The artificial neural network-based LM backpropagation optimization algorithm operates using data based on the ratio of testing (13%), validation (17%), and training (70%). This stochastic computing work presents an activation log-sigmoid function based LM backpropagation optimization algorithm, in which tens of neurons and hidden and output layers are used for solving the learning language model. The overlapping of the results and the small computed absolute errors, which range from 10−3 to 10−10 and from 106 to 108 for each model class, indicate the accuracy of the artificial neural network-based LM backpropagation optimization algorithm. Furthermore, each model case’s regression performance is evaluated as if it were an ideal model. In addition, function fitness and histogram are used to validate the dependability of the algorithm. Numerical approaches and artificial neural networks are an excellent combination for fluid dynamics, and this could lead to new advancements in many domains. The findings of this research could contribute to the optimization of fluid systems, resulting in increased efficiency and production across various technical domains.

Comprehensive investigation of isotherm, RSM, and ANN modeling of CO2 capture by multi-walled carbon nanotube

Chemical vapor deposition was used to produce multi-walled carbon nanotubes (MWCNTs), which were modified by Fe–Ni/AC catalysts to enhance CO2 adsorption. In this study, a new realm of possibilities and potential advancements in CO2 capture technology is unveiled through the unique combination of cutting-edge modeling techniques and utilization of the recently synthesized Fe–Ni/AC catalyst adsorbent. SEM, BET, and FTIR were used to analyze their structure and morphology. The surface area of MWCNT was found to be 240 m2/g, but after modification, it was reduced to 11 m2/g. The modified MWCNT showed increased adsorption capacity with higher pressure and lower temperature, due to the introduction of new adsorption sites and favorable interactions at lower temperatures. At 25 °C and 10 bar, it reached a maximum adsorption capacity of 424.08 mg/g. The optimal values of the pressure, time, and temperature parameters were achieved at 7 bar, 2646 S and 313 K. The Freundlich and Hill models had the highest correlation with the experimental data. The Second-Order and Fractional Order kinetic models fit the adsorption results well. The adsorption process was found to be exothermic and spontaneous. The modified MWCNT has the potential for efficient gas adsorption in fields like gas storage or separation. The regenerated M-MWCNT adsorbent demonstrated the ability to be reused multiple times for the CO2 adsorption process, as evidenced by the study. In this study, a feed-forward MLP artificial neural network model was created using a back-propagation training approach to predict CO2 adsorption. The most suitable and efficient MLP network structure, selected for optimization, consisted of two hidden layers with 25 and 10 neurons, respectively. This network was trained using the Levenberg–Marquardt backpropagation algorithm. An MLP artificial neural network model was created, with a minimum MSE performance of 0.0004247 and an R2 value of 0.99904, indicating its accuracy. The experiment also utilized the blank spreadsheet design within the framework of response surface methodology to predict CO2 adsorption. The proximity between the Predicted R2 value of 0.8899 and the Adjusted R2 value of 0.9016, with a difference of less than 0.2, indicates a high level of similarity. This suggests that the model is exceptionally reliable in its ability to predict future observations, highlighting its robustness.

Comparison of different supervised learning algorithms for position analysis of the slider-crank mechanism

Slider-crank mechanisms have a wide range of applications in machine design, primarily for converting rotary motion into reciprocating motion, as seen in internal combustion engines, pumps, compressors, and human-powered vehicles. Due to their widespread usage, analytical and graphical solutions for the position analysis of slider-crank mechanisms are introduced in various textbooks and lecture notes. Recently, artificial neural network algorithms have been applied to various design applications across different research fields, and mechanism design is no exception. However, investigations into the application of neural network algorithms specifically to slider-crank mechanisms are quite limited. In this study, the analytical position analysis of the slider-crank mechanism is presented, and datasets are obtained first. Subsequently, the application of following three different supervised learning algorithms to the position analysis of the slider-crank (R-RRT) mechanism is investigated using analytical solution datasets: the Levenberg-Marquardt Backpropagation (LM) algorithm, Bayesian Regularization (BR) algorithm, and Scaled Conjugate Gradient Descent (SCG) algorithm. These three algorithms were chosen due to their distinct characteristics. The main objectives of this study are to highlight the differences among various neural network algorithms' applications and to understand the applicability and suitability of different neural network algorithms for kinematic position analysis in mechanisms. The results indicate that the Bayesian regularization algorithm yields the best results, while the Levenberg-Marquardt Backpropagation algorithm exhibits the best performance.

Neural Network With Cloud-Based Training for MTPA, Flux-Weakening, and MTPV Control of IPM Motors and Drives

An interior permanent magnet (IPM) motor is a prime electric motor used in electric vehicles (EVs), robots, and electric drones. In these applications, maximum torque per ampere (MTPA), flux-weakening, and maximum torque per volt (MTPV) techniques play a critical role in the efficient and reliable torque control of an IPM motor. Although several approaches have been proposed and developed for this purpose, each has its specific limitations. The objective of this paper is to develop a neural network (NN) method to determine MTPA, flux-weakening, and MTPV operating points for the most efficient torque control of the motor over its full speed range. The NN is trained offline by using the Levenberg-Marquardt backpropagation algorithm, which avoids the disadvantages associated with online NN training. A cloud computing system is proposed for routine offline NN training, which enables the lifetime adaptivity and learning capabilities of the offline-trained NN and overcomes the computational challenges related to online NN training. In addition, for the proposed NN mechanism, training data are collected and stored in a highly random manner, which makes it much more feasible and efficient to implement lifetime adaptivity than any other methods. The proposed method is evaluated via both simulation and hardware experiments, which shows the great performance of the NN-based MTPA, MTPV, and flux-weakening control for an IPM motor over its full speed range. Overall, the proposed method can achieve a fast and accurate current reference generation with a simple NN structure, for optimal torque control of an IPM motor.

Dynamic neural network modeling of thermal environments of two adjacent single-span greenhouses with different thermal curtain positions

In order to produce marketable yield, scientific methodologies must be used to forecast the greenhouse microclimate, which is affected by the surrounding macroclimate and crop management techniques. The MATLAB tool NARX was used in this study to predict the strawberry yield, indoor air temperature, relative humidity, and vapor pressure deficit using input parameters such as indoor air temperature, relative humidity, solar radiation, indoor roof temperature, and indoor relative humidity. The data were normalized to improve the accuracy of the model, which was developed using the Levenberg–Marquardt backpropagation algorithm. The accuracy of the models was determined using various evaluation metrics, such as the coefficient of determination, mean square error, root mean square error, mean absolute deviation, and Nash–Sutcliffe efficiency coefficient. The results showed that the models had a high level of accuracy, with no significant difference between the experimental and predicted values. The VPD model was found to be the most important as it influences crop metabolic activities and its accuracy can be used as an indoor climate control parameter.

Levenberg–Marquardt backpropagation neural networking (LMB-NN) analysis of hydrodynamic forces in fluid flow over multiple cylinders

The mathematical formulation of the flowing liquid stream around and through confined multiply connected domains brings a complex differential system. Due to this, one cannot provide a complete description of flow field properties. The current pagination provides a numerical remedy by the use of artificial intelligence in this direction. To be more precise, fluid is considered in the rectangular channel. The circular, square, and triangular-shaped cylinders are positioned as an obstacle to the continuous fluid. The channel’s left wall is viewed as an inlet and two different velocity profiles are introduced at an inlet that are constant and parabolic profile. To discretize the computational domain, hybrid meshing is used. The variance in basic variables, namely, the velocity of the liquid and the distribution of the liquid pressure, is recorded via graphs. The nine different meshed grades are tested for the drag and lift coefficients around the right-angle triangle, square, and circular barrier. The neural networking model is constructed by using 99 datasets of sample values for drag coefficient when characteristic length, the density of fluid, the dynamic viscosity of the fluid, and mean flow velocity are taken as inputs. The training of the neural network takes up 69 slots (about 70%), while the testing and validation of the neural network each take up 15 slots (15%). The Levenberg–Marquardt backpropagation algorithm is used for training. We have observed that for the parabolic profile, the drag coefficient is higher in intensity for each obstacle compared to the constant profile, while the lift coefficient shows opposite patterns.

A Third-order Two Stage Numerical Scheme and Neural Network Simulations for SEIR Epidemic Model: A Numerical Study

This study focuses on the cutting-edge field of epidemic modeling, providing a comprehensive investigation of a third-order two-stage numerical approach combined with neural network simulations for the SEIR (Susceptible-Exposed-Infectious-Removed) epidemic model. An explicit numerical scheme is proposed in this work for dealing with both linear and nonlinear boundary value problems. The scheme is built on two grid points, or two time levels, and is third-order. The main advantage of the scheme is its order of accuracy in two stages. Third-order precision is not only not provided by most existing explicit numerical approaches in two phases, but it also necessitates the computation of an additional derivative of the dependent variable. The proposed scheme's consistency and stability are also examined and presented. Nonlinear SEIR (susceptible-exposed-infected-recovered) models are used to implement the scheme. The scheme is compared with the non-standard finite difference and forward Euler methods that are already in use. The graph shows that the plan is more accurate than non-standard finite difference and forward Euler methods that are already in use. The solution obtained is then looked at through the lens of the neural network. The neural network is trained using an optimization approach known as the Levenberg-Marquardt backpropagation (LMB) algorithm. The mean square error across the total number of iterations, error histograms, and regression plots are the various graphs that can be created from this process. This work conducts thorough evaluations to not only identify the strengths and weaknesses of the suggested approach but also to examine its implications for public health intervention. The results of this study make a valuable contribution to the continuously developing field of epidemic modeling. They emphasize the importance of employing modern numerical techniques and machine learning algorithms to enhance our capacity to predict and effectively control infectious diseases. Doi: 10.28991/ESJ-2024-08-01-023 Full Text: PDF

Artificial neural network-based prediction model of elastic floor response spectra incorporating dynamic primary-secondary structure interaction

The evaluation of the Floor Response Spectrum (FRS) holds paramount significance in assessing the seismic behavior of secondary structures. Precise FRS prediction empowers engineers to make informed decisions concerning structural design, retrofitting, and safety precautions. This study aims to scrutinize the impact of dynamic interaction between primary and secondary structures on FRS. Both the elastic primary structure (PS) and elastic secondary structure (SS) employ a single-degree-of-freedom (SDOF) system. Governing motion equations for both coupled (with dynamic interaction) and uncoupled (without dynamic interaction) systems are formulated and solved numerically. The study investigates how variations in the vibration period of PS (Tp), tuning ratio (Tr), mass ratio (μ), and damping ratio (ξs) of SS influence FRS. The FRS impact remains minimal at μ = 0.001 (0.1%); however, with increasing mass ratio, PS-SS dynamic interaction significantly affects SS's spectral acceleration response. Coupled analysis is crucial only for secondary structures tuned to the primary structure's vibration period (0.8≤Tr≤1.2). This study utilizes two-layer feed-forward Artificial Neural Networks (ANNs) for FRS prediction. The Levenberg-Marquardt (LM) backpropagation (BP) algorithm trains the network using a comprehensive dataset. In summary, it is evident that the ANNs, once trained, enable accurate prediction of the FRS, exhibiting a R2 of 99%. Additionally, a design expression is formulated utilizing the ANN model and subsequently compared with the existing formulation.

A novel solution for the optimal reactive power dispatch problem using an artificial neural network integrated with the firefly optimization algorithm

This article applies a novel intelligence technique to solve power system issues faced daily. Compensation for reactive power is a significant issue faced by power system operators in research. The solution can be obtained by handling a multi-objective task and multiconstraints by reducing the active power loss and minimizing the voltage deviation at the load end. The novelty of the research focuses on integrating artificial neural network techniques with the firefly algorithm, a novel optimization algorithm for attaining an objective function. The Levenberg–Marquardt back-propagation algorithm is most suited for proper tuning of the control variables. The objective of this research can be attained by appropriately tuning the control variables connected with the IEEE test bus systems, which helps to maximally improve the voltage profile. Existing research studies have focused on reactive power management, which is attained by solving optimal reactive power flow problems employing nature-inspired approach techniques such as the symbiotic organism search algorithm, the cuckoo search algorithm, the black hole algorithm, the krill herd algorithm, and whale optimization. The evolving strategy, the firefly algorithm (FFA), minimizes the multiconstraint functions more competently and effectively than any conventional algorithm. To showcase the strength of the firefly algorithm incorporating AI, it is examined on standard IEEE test bus systems, namely, the 14-, 30-, and 58-bus networks. The obtained results quantify the effectiveness of the proposed methodology, that is, the artificial intelligence technique implementing the firefly algorithm gives better results than conventional methods.

Detailed experimentation and prediction of thermophysical properties in lauric acid-based nanocomposite phase change material using artificial neural network

The thermal conductivity (TC) of a nanocomposite phase change material (NPCM) may be improved by adding nanostructured materials to a Phase Change Material (PCM). To assess the heat transfer rate during the process of phase change, such as melting and freezing, an accurate TC prediction of NPCM is required. A Field Emission Scanning Electron Microscope (FESEM) was utilized to examine the nanoparticle morphological study, and X-Ray Diffraction (XRD) analysis evaluated the crystalline structure. NPCMs were verified using Fourier Transform Infrared Spectroscopy (FTIR). The goal of this research is to create an Artificial Neural Network (ANN) that guesses the TC and viscosity of Lauric Acid (LA) embedded with dispersed copper oxide (CuO) and aluminium oxide (Al2O3). A multi-layered feed-forward ANN (MLFFANN) is trained using the Levenberg-Marquardt (LM) backpropagation algorithm. There are 130 experimental datasets in total, obtained from experiments with nanoparticle mass fractions ranging from 1.25 to 10 wt%. The minimum mean square error (MSE) for TC and viscosity is 4.6815 × 10−5 and 2.4681 × 10−5, respectively. The average absolute deviation (AAD) for TC and viscosity is 0.004249 and 0.003596, respectively, while the mean absolute percentage error (MAPE) is 2.1835 % and 4.8197 % for TC and viscosity, and the correlation coefficients(R) are 0.992 and 0.975, respectively. The largest percentage variation between experimental values and ANN computed values for the liquid and solid phases, respectively, is 3.54 % and 0.832 %. It demonstrates that the constructed ANN prediction model predicts the increased TC and viscosity of NPCM for different nanoparticle loadings, temperatures, and oxide nanoparticles.

Predicting the strengths of date fiber reinforced concrete subjected to elevated temperature using artificial neural network, and Weibull distribution

Date palm fiber (DPF) is normally used as fiber material in concrete. Though its addition to concrete leads to decline in durability and mechanical strengths performance. Additionally, due to its high ligno-cellulose content and organic nature, when used in concrete for high temperature application, the DPF can easily degrade causing reduction in strength and increase in weight loss. To reduce these effects, the DPF is treated using alkaline solutions. Furthermore, pozzolanic materials are normally added to the DPF composites to reduce the effects of the ligno-cellulose content. Therefore, in this study silica fume was used as supplementary cementitious material in DPF reinforced concrete (DPFRC) to reduce the negative effects of elevated temperature. Hence this study aimed at predicting the residual strengths of DPFRC enhanced/improved with silica fume subjected to elevated temperature using different models such as artificial neural network (ANN), multi-variable regression analysis (MRA) and Weibull distribution. The DPFRC is produced by adding DPF in proportions of 0%, 1%, 2% and 3% by mass. Silica fume was used as partial substitute to cement in dosages of 0%, 5%, 10% and 15% by volume. The DPFRC was then subjected to elevated temperatures between 200 and 800 °C. The weight loss, residual compressive strength and relative strengths were measured. The residual compressive strength and relative strength of the DPFRC declined with addition of DPF at any temperature. Silica fume enhanced the residual and relative strengths of the DPFRC when heated to a temperature up to 400 °C. To forecast residual compressive strength (RCS) and relative strength (RS), we provide two distinct ANN models. The first layer's inputs include DPF (%), silica fume (%), temperature (°C), and weight loss (%). The hidden layer is thought to have ten neurons. M-I is the scenario in which we use RCS as an output, whereas M-II is the scenario in which we use RS as an output. The ANN models were trained using the Levenberg–Marquardt backpropagation algorithm (LMBA). Both neural networking models exhibit a significant correlation between the predicted and actual values, as seen by their respective R = 0.99462 and R = 0.98917. The constructed neural models M-I and M-II are highly accurate at predicting RCS and RS values. MRA and Weibull distribution were used for prediction of the strengths of the DPFRC under high temperature. The developed MRA was found to have a good prediction accuracy. The residual compressive strength and relative strength followed the two-parameter Weibull distribution.

A highly accurate model for prediction of thermal conductivity of carbon-based nano-enhanced PCMs using an artificial neural network

Nano-PCMs, which contain nanostructured materials, can enhance the low thermal conductivity of phase change materials (PCMs). It is crucial to predict the precise thermal conductivity of nano-PCM to assess the heat transfer during phase change procedures such as melting and solidification. In this study, artificial neural network (ANN) and response surface method (RSM) were used to develop a model for predicting the thermal conductivity of carbon-based nano-enhanced phase change materials (NEPCMs) using 482 experimental samples collected from various datasets. The carbon-based nano-particles were SWCNTs, MWCNTs, graphite, graphene, and (CNFs). Six input parameters were considered with varying temperature, mass fractions, sizes, thermal conductivities of PCMs and nano-particles, and the phase of PCM. The ANN was designed using a multi-layered feed-forward structure and Levenberg–Marquardt back-propagation algorithm with one hidden layer consisting of eight neurons. The transfer function was varied to achieve optimal performance. It was revealed that the predictive capacity of the ANN model is greater than that of the RSM model based on their corresponding the coefficient of determination (R2) and root mean square error (RMSE) values. The developed ANN achieved a R2 value of 0.99, while the RSM method model achieved an R2 value of 0.79.

Implementation of fuzzy associative memory toward optimizing a neural network model to predict total iron binding capacity

ObjectivesDetermining the optimal number of hidden layer neurons in a neural network (NN) is an important issue. In this research, a fuzzy associative memory (FAM) model will be developed to determine the hidden neuron of neural network architecture. MethodsThe material for this research is data from chronic kidney disease (CKD) patients on hemodialysis to predict total iron binding capacity (TIBC). The research begins by choosing the most appropriate backpropagation algorithm. The FAM system was developed using the product-correlation coding operator, the product-max composition relation, and a combination of winner-take-all and weighted average. Testing is done by calculating the average error (MSE) of training and testing, as well as the average error for gradients. ResultsWe selected 12 alternative network architectures. The Levenberg-Marquardt backpropagation algorithm was chosen for its best performance. In testing the training data, the average percentage of errors was 7.16% for MSE and 12.93% for gradients. The test results of the FAM system show an average error of 0%. The test results on the test data show an average error for MSE of 7.67% and an average error for gradients of 8.55%. ConclusionBesides being able to predict the MSE and gradient for a particular NN architecture, FAM can also determine the NN architectural model that provides the optimal combination of MSE and gradient. Further research will improve the performance of the FAM system.

Darcy-Forchheimer hybrid nanofluid flow over the rotating Riga disk in the presence of chemical reaction: Artificial neural network approach

The aim of present study is to examine the augmentation of thermal energy transfer in hybrid nanofluid flow caused by a rotating Riga disk in the presence of thermal radiation and chemical reaction. The silver and aluminium oxide nanoparticles are used to examine the thermal effect of water base fluid. The Darcy-Forchheimer model is considered to endorse the inertial and porous media effects and makes the model more realistic from the physical scenario. Levenberg-Marquardt backpropagation algorithm is considered to analyze the hybrid nanofluid’s properties. Using scaling group transformations, the governing partial differential equations are transformed into a system of ordinary differential equations. Resulting ordinary differential equations are solved numerically by applying a suitable shooting technique by MATLAB. The results obtained for the governing differential equations have been incorporated into a dataset on which the neural network has been trained. The effects of physical parameters have been analyzed for velocity, temperature, and concentration profiles. The determination, designing, convergence, verification, and stability of the Levenberg-Marquardt backpropagation neural network algorithm are validated on the assessment of achieved accuracy through performance, fit, regression, and error histogram plots for the discussed hybrid nanofluid. It is observed that fluid velocity reduces for enhanced Darcy-Forchheimer number, magnetic parameters and boosted for enhanced modified Hartmann number. Temperature profile increases by increasing the Brownian motion and thermophoresis parameters.

The impact of supply chain management on a company's operation and decision based on the multidimensional data analysis of upstream and downstream industry market states.

This study provides data analysis support for the entire enterprise procurement management process, thereby improving the management effectiveness of supply chain operations. It analyzes upstream and downstream industry market status data in the supply chain and various primary data in enterprise management activities. By utilizing the Delphi method to screen and verify multimode market status data indicators, which significantly impact upstream and downstream industries in multiple rounds, 28 types of market status data were selected for analysis. This analysis aimed to investigate the effect of supply chain management on operational decisions within the company. The data reduction method based on adaptive statistics was the most effective in revealing the market status and promoting efficient operation decision-making based on supply chain management. This study also suggests a brand-new technique for measuring supply chain performance based on the Levenberg-Marquardt Back Propagation (LMBP) algorithm, offering a more impartial manner of doing so. The performance evaluation results showed a maximum error level of less than 0.4% when paired with empirical analysis. The proposed optimization model provides strategic guidance for optimizing supply chain management and improving overall performance.