Mean Absolute Percentage Error Values Research Articles

Ultra-low cycle fatigue life prediction of stainless steel based on transfer learning guided artificial neural network

Determining ultra-low cycle fatigue (ULCF) life of stainless steel typically involves laborious and time-consuming tests. While machine learning offers an efficient solution for fatigue life prediction, the inherent demand for sufficient training data remains unaddressed. To solve this challenge, a novel method to predict ULCF life using small datasets was proposed by combining artificial neural network (ANN) and transfer learning (TL). A TL-guided ANN (TLNN) framework was developed, wherein the knowledge gained from a pre-trained ANN model for structural steels was transferred to ULCF life prediction of stainless steel. The TLNN model exhibits enhanced predictive capabilities for stainless steel under small amounts of training datasets, with mean R2 (coefficient of determination) value of 0.88 and average MAPE (mean absolute percentage error) value of 25.52%. In comparison, the directly trained ANN model shows lower performance, possessing that the average R2 and MAPE are 0.69 and 47.58%, respectively. We pioneeringly explored and found that adding synthetic data enhancement had no positive effect on the predictive ability of the TLNN model. Furthermore, the dependence of predictive capacity by the TLNN model on the number of training data availability was studied. The prediction process of the TLNN model was interpreted by SHAP (SHapley Additive exPlanations) method. In general, the developed TLNN model can efficiently predict the ULCF life of stainless steel with high accuracy and reduce the cost of consecutive fatigue property evaluation.

Prediction of the level of crime cases using multiple linear regression in the city of Pontianak

This study aims to develop a predictive model for the crime rate in the Police Resort Area of Kota (POLRESTA) Pontianak using the Multiple Linear Regression method based on secondary data obtained from the Criminal Investigation Unit of POLRESTA Pontianak. The utilization of descriptive statistical techniques and data visualization aids in identifying relevant features that enrich the information within the model. The evaluation results indicate that this model performs well in both modeling and predicting crime rates in Kota Pontianak. Despite the variations in error rates between training and testing data, the model still demonstrates its proficiency in predicting known data. The testing results also reveal that the Mean Absolute Percentage Error (MAPE) values for each crime category exhibit variations in the testing dataset, with MAPE for "Berat" increasing to 12.91%, MAPE for "Sedang" increasing to 30.11%, and MAPE for "Ringan" increasing to 26.59%. Consequently, this study concludes that the Multiple Linear Regression method holds potential as an effective tool for decision-making and the development of strategies to combat criminal activities in Kota Pontianak

A Hybrid Deep Learning Model to Estimate the Future Electricity Demand of Sustainable Cities

Rapid population growth, economic growth, and technological developments in recent years have led to a significant increase in electricity consumption. Therefore, the estimation of electrical energy demand is crucial for the planning of electricity generation and consumption in cities. This study proposes a hybrid deep learning model that combines convolutional neural network (CNN) and long short-term memory (LSTM) techniques, both of which are deep learning techniques, to estimate electrical load demand. A hybrid deep learning model and LSTM model were applied to a dataset containing hourly electricity consumption and meteorological information of a city in Türkiye from 2017 to 2021. The results were evaluated using mean absolute percent error (MAPE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) metrics. The proposed CNN-LSTM hybrid model was compared to the LSTM model, with lower MAPE, MAE, and RMSE values. Furthermore, the CNN-LSTM model exhibited superior prediction performance with an R2 value of 0.8599 compared to the LSTM model with an R2 value of 0.8086. These results demonstrate the effectiveness of the proposed deep learning model in accurately estimating future electrical load demand to plan electricity generation for sustainable cities.

Spectro-ViT: A vision transformer model for GABA-edited MEGA-PRESS reconstruction using spectrograms

This study investigated the use of a Vision Transformer (ViT) for reconstructing GABA-edited Magnetic Resonance Spectroscopy (MRS) data from a reduced number of transients. Transients refer to the samples collected during an MRS acquisition by repeating the experiment to generate a signal of sufficient quality. Specifically, 80 transients were used instead of the typical 320 transients, aiming to reduce scan time. The 80 transients were pre-processed and converted into a spectrogram image representation using the Short-Time Fourier Transform (STFT). A pre-trained ViT, named Spectro-ViT, was fine-tuned and then tested using in-vivo GABA-edited MEGA-PRESS data. Its performance was compared against other pipelines in the literature using quantitative quality metrics and estimated metabolite concentration values, with the typical 320-transient scans serving as the reference for comparison. The Spectro-ViT model exhibited the best overall quality metrics among all other pipelines against which it was compared. The metabolite concentrations from Spectro-ViT's reconstructions for GABA+ achieved the best average R2 value of 0.67 and the best average Mean Absolute Percentage Error (MAPE) value of 9.68%, with no significant statistical differences found compared to the 320-transient reference. The code to reproduce this research is available at https://github.com/MICLab-Unicamp/Spectro-ViT

XGBoost machine learning assisted prediction of the mechanical and fracture properties of unvulcanized and dynamically vulcanized PP/EPDM reinforced with clay and halloysite nanoparticles

AbstractPolymer nanocomposites have found wide industrial applications, necessitating optimal mechanical and fracture properties evaluation, traditionally done through costly experimental methods. This study employs machine learning, particularly XGBoost, to predict properties like tensile and fracture properties swiftly, aiding material innovation across industries. The research investigates unvulcanized and vulcanized polypropylene (PP)/ethylene propylene diene monomer (EPDM) reinforced with clay and halloysite nanoparticles (HNT), analyzing fracture properties via essential work of fracture (EWF). Experimental design selects tests, and an XGBoost model predicts tensile strength and modulus, strain at break, EWF, and non‐EWF based on EPDM and nanoparticle percentages, composite and nanoparticle types. The model accurately predicts tensile strength and modulus but less so for strain at break, EWF, and non‐EWF. Mean Absolute Percentage Error values for training/test are 0.49/1.21, 1.05/1.55, 34.21/42.76, 3.02/14.35, and 2.89/3.78, with determination coefficients of 0.99/0.98, 0.99/0.97, 0.97/0.91, 0.97/0.79, and 0.92/0.73. Nanoparticles mainly affect outputs, with EPDM secondarily impactful, while composite and nanoparticle types exhibit similar significance. The best‐performing polymer nanocomposite is a dynamically vulcanized one containing 10 wt% EPDM and 3 wt% clay, achieving tensile strength of 25.070 MPa, tensile modulus of 261.170 MPa, EWF of 75.300 N/mm, and non‐EWF of 10.150 N/mm2.Highlights The effects of ethylene propylene diene monomer (EPDM), clay and halloysite nanoparticles on the mechanics of polypropylene‐based nanocomposites. Essential work of fracture (EWF) was used to study the fracture properties. Machine learning was employed to predict all mechanical characteristics. The vulcanization process improved all mechanical characteristics. The best compound: vulcanized one containing 10 wt% EPDM and 3 wt% clay.

Investigation of a solar-assisted methanol steam reforming system: Operational factor screening and computational fluid dynamics data-driven prediction

The development of solar-to-fuel technologies has received significant attention while facing the growing interest in clean energy production. To accurately evaluate and optimize the solar-assisted thermochemical hydrogen production process, a reliable prediction method that ensures stable accuracy is essential. We utilized a gray correlation analysis (GRA) and a multi-objective genetic algorithm (GA) model to optimize the operating parameters of a methanol steam reforming system. Computational fluid dynamics simulations provided the response values for key parameters, including reactant conversion rate, syngas product yield, and CO selectivity. Additionally, the Taguchi’s method and analysis of variance (ANOVA) were employed to assess the impact of operating parameters on these target outputs. The results demonstrate that the proposed GA-Back propagation neural networks (GA-BPNN) model significantly outperforms traditional prediction models in accuracy, exhibiting robustness and generalization. The mean absolute percentage error (MAPE) of methanol conversion, hydrogen yield, and CO selectivity is 3.20 %, 4.69 % and 3.53 %. The GRA-GA-BPNN model shows a slight decline, while still maintains reliable prediction accuracy, with the MAPE value of 5.76 %, 5.11 %, and 10.4 %. This operational factor screening-based prediction model proves useful for large-scale data-driven predictions of solar thermochemical systems, especially under complex and variable external conditions and internal human interventions.

Peramalan Jumlah Kebutuhan Packaging Papper Photocopy (PPC) PT. Y Menggunakan Metode Moving Avarage dan Exponential Smoothing

In the era of globalization, industry and trade in Indonesia face fierce competition. Technological advances require quality human resources from formal and non-formal educational institutions with supporting facilities. This research focuses on solving safety stock problems using the Exponential Smoothing and Moving Average methods. Due to the current changing market demand, the results of data processing carried out using the Quality Management (QM) program for Windows show that the forecasting method with the Moving Average approach has the highest Mean Absolute Percentage Error (MAPE) value of 16.03%, while the Exponential Smoothing method has a MAPE value of 13.7%. So with this, the lowest MAPE value is in the forecasting method with the Exponential Smoothing approach of 13.7%. Keywords: Demand Forecasting, Exponential Smoothing Method, Moving Average Method, MAPE, Safety Stock

Apple Watch 6 vs. Galaxy Watch 4: A Validity Study of Step-Count Estimation in Daily Activities.

The purpose of this study was to examine the validity of two wearable smartwatches (the Apple Watch 6 (AW) and the Galaxy Watch 4 (GW)) and smartphone applications (Apple Health for iPhone mobiles and Samsung Health for Android mobiles) for estimating step counts in daily life. A total of 104 healthy adults (36 AW, 25 GW, and 43 smartphone application users) were engaged in daily activities for 24 h while wearing an ActivPAL accelerometer on the thigh and a smartwatch on the wrist. The validities of the smartwatch and smartphone estimates of step counts were evaluated relative to criterion values obtained from an ActivPAL accelerometer. The strongest relationship between the ActivPAL accelerometer and the devices was found for the AW (r = 0.99, p < 0.001), followed by the GW (r = 0.82, p < 0.001), and the smartphone applications (r = 0.93, p < 0.001). For overall group comparisons, the MAPE (Mean Absolute Percentage Error) values (computed as the average absolute value of the group-level errors) were 6.4%, 10.5%, and 29.6% for the AW, GW, and smartphone applications, respectively. The results of the present study indicate that the AW and GW showed strong validity in measuring steps, while the smartphone applications did not provide reliable step counts in free-living conditions.

LQ45 Stock Price Forecasting: A Comparison Study of Arima(p,d,q) and Holt-Winter Method

The Holt-Winter method and ARIMA(p,d,q) are two frequently used forecasting techniques. When using ARIMA, errors are expected to be connected with earlier errors because it is based on data correlation with prior data (autoregressive) (moving average). The Holt-Winter model comes in two different forms: Multiplicative Holt-Winter and Additive Holt-Winter. No one has ever attempted to compare combined time series and cross-section data, despite the fact that there has been a great deal of prior study on ARIMA and Holt-Winter. In a combined time-series and cross-section dataset, the accuracy rates of Holt-Winter and ARIMA(p,d,q) will be compared in this study. LQ45 stock prices are used because they track the performance of 45 stocks with substantial liquidity, sizable market caps, and solid underlying businesses. The Mean Absolute Percentage Error (MAPE) method is used to gauge accuracy. This study contributes to MAPE exploration by using a Boxplot diagram from cross-sectional data. With the Boxplot diagram, we can see the MAPE spread, the MAPE's center point, and the presence of outliers from the MAPE of LQ45 stock. According to the findings of this empirical study, the average error rate for predicting LQ45 stock prices using ARIMA is 7,0390%, with a standard deviation of 7,7441%; for multiplying Holt-Winter, it is 29,3919%, with a standard deviation of 25,7571%; and for additive Holt-Winter, it is 18,0463%, with a standard deviation of 18,3504%. Apart from numerical comparisons, it can also be seen visually, based on the Boxplot diagram, that the MAPE of ARIMA(p,d,q) is more focused than Holt-Winter. In addition, in terms of accuracy distribution, it can be seen that the MAPE accuracy of the ARIMA method produces four outliers. Based on the MAPE accuracy rate, we conclude that Holt-Winter has a bigger error based on the MAPE value than ARIMA(p,d,q) at forecasting LQ45 stock prices.

Digital Twin Technology in the Gas Industry: A Comparative Simulation Study

Continuous innovation is essential in the urban gas industry to achieve the stability of energy supply and sustainability. The continuous increase in the global demand for energy indicates that the urban gas industry plays a crucial role in terms of stability, the economy, and the environmental friendliness of the energy supply. However, price volatility, supply chain complexity, and strengthened environmental regulations are certain challenges faced by this industry. In this study, we intend to overcome these challenges by elucidating the application of digital twin technology and by improving the performance of the prediction models in the gas industry. The real-time data and simulation-based predictions of pressure fluctuations were integrated in terms of pressure control equipment. We determined the contribution of this data integration to enhancing the operational efficiency, safety, and sustainable development in the gas industry. The summary of the results highlights the superior predictive performance of the autoregressive integrated moving average (ARIMA) model. It exhibited the best performance across all evaluation indices—mean absolute percentage error (MAPE), root mean square error (RMSE), and the coefficient of determination (R2)—when compared with the raw data. Specifically, the ARIMA model demonstrated the lowest RMSE value of 0.01575, the lowest MAPE value of 0.00609, and the highest R2 value of 0.94993 among the models evaluated. This indicates that the ARIMA model outperformed the other models in accurately predicting the outcomes. These findings validate that the integration of digital twin technology and prediction models can innovatively improve the maintenance strategy, operational efficiency, and risk prediction in the gas industry. Predictive maintenance models can help prevent significant industrial risks, such as gas leak accidents. Moreover, the integration of digital twin technology and predictive maintenance models can significantly enhance the safety and sustainability in the gas industry. The proposed innovative method of implementing digital twin technology and improved prediction models lays a theoretical foundation for sustainable development that can be applied to other industries with high energy consumption.

Spatial mapping and driving factor Identification for salt-affected soils at continental scale using Machine learning methods

Soil salinization is one of the most serious land-degrading threats globally, particularly in Asia, where there are 21 countries, e.g., Kazakhstan, China, Iran, and Indonesia, lack of accurate spatial information of salt-affected soils currently. Mapping the distribution of salt-affected soils and identifying their main driving factors is critical for sustainable development. Current studies for mapping the spatial distribution of salt-affected soils focused on recognizing the soil salinity by using the indicator of electrical conductivity of saturated soil paste extract (ECe), while cannot distinguish the soil sodicity. Meanwhile, the absence of uncertainty assessment is another problem for large-scale studies. To address these gaps, we developed an effective spatial prediction model that considered both soil salinity and sodicity using machine learning (ML) models and soil salinization data. This study used ECe, pH, and exchangeable sodium percent (ESP) as indicators, with sample sizes of 22,803, 30,821, and 12,961, respectively, to produce salt-affected soil maps with uncertainty assessments in Asia, and conducted the analysis of the drivers of soil salinization. The ML models involved cubist, random forest, quantile regression forest, quantile regression neural network, and a simple multiple linear regression model for comparison. The results showed that among these models, quantile regression forest exhibited the best performance, with mean absolute percentage error values of 2.54 %∼52.06 %, coefficient of determination values of 0.40 ∼ 0.74, and Nash-Sutcliffe efficiency values of 0.36 ∼ 0.71. The predicted maps, with 1 km resolution, demonstrated minimal uncertainty across over 60.4 % of the study area. Approximately 466.36 million hectares of topsoil (0–30 cm) and 377.68 million hectares of subsoil (30–100 cm) in Asia are salt-affected, mainly concentrated in arid, semi-arid, and coastal regions. Climatic factors (the annual precipitation and maximum temperature), topography (digital elevation model and slope), and salinity index V (SI5) were identified as the main drivers of soil salinization. Overall, this study provides important guidelines for soil salinization management and achieving ecological sustainability.

A Transcendental LASSO Function for Combining Machine Learning and Statistical Model Forecasts

The aim of the study is the development of methodology for accurate estimation of electric vehicle demand; which is paramount regarding various aspects of the firms decision-making such as optimal price, production level, and corresponding amounts of capital and labor; as well as supply chain, inventory control, capital financing, and operational expenses management. The forecasting methods utilized include statistical techniques (autoregressive integrated moving average [ARIMA], and polynomial regression), machine learning (nonlinear autoregressive neural network [NAR]), deep learning (long short-term memory [LSTM]), hybrid and combination forecasting. With regard to the latter method, our study experiments with four different combining model approaches, including the introduction of an original, novel combining method with the employment of a transcendental LASSO function, which is used to form combinations of forecasts generated by the NAR, ARIMA, and polynomial regression models. The LASSO-based combining model proved superior to all other models, for the majority of forecast error statistics; where the root mean square error (RMSE) and mean absolute percentage error (MAPE) values are 4.5% and 8% respectively lower than the average level of the component model forecasts. The major implications of our empirical findings are that greater accuracy in demand forecasting can be achieved with a combining model approach, rather than reliance on any particular, singular model. Furthermore, given its superior performance, the employment of the studys LASSO-based combining model to forecast electric vehicle demand may lead to optimal firm decision-making over a range of organizational facets, which is predicated on accurate demand function estimation.

Digital twin-driven architecture for AIoT-based energy service provision and optimal energy trading between smart nanogrids

This article explores integrating digital twin technology and blockchain within smart grids to optimize energy trading among prosumers and consumers in smart nanogrids. Our platform employs a multi-objective optimization strategy, including Particle Swarm Optimization (PSO), to delineate energy trading routes between nanogrids, optimizing parameters such as route distance, surplus renewable energy, and energy power loss. Our platform ensures efficient and effective energy trading services by meticulously considering factors such as surplus energy amount, energy price, route distance, and time. The proposed digital twin-based architecture comprises seven layers, each tailored to address specific functionalities and services for energy management within smart nanogrids. At the apex lies the application layer (digital twin services), leveraging the digital twin's capabilities to optimize energy trading, manage surplus energy, and efficiently meet energy demand. This layer facilitates informed decision-making and resource optimization. Integrating a digital twin-driven architecture with a blockchain-based platform tackles challenges inherent in decentralized energy trading. The digital twin offers real-time energy resource monitoring and optimisation, ensuring efficient utilisation and autonomous decision-making. Concurrently, leveraging blockchain technology ensures secure and transparent transactions, fostering trust among participants and facilitating peer-to-peer energy exchange. Task generation, device virtualization, task mapping, scheduling on edge devices, and task assignment layers further streamline task execution and resource utilization, enhancing the efficiency of energy management processes. The predictive optimal energy control layer also orchestrates the entire architecture, enabling predictive and optimized energy control within smart nanogrids. Furthermore, the Security as a Service (SECaaS) layer enhances security and trustworthiness using blockchain technology, incorporating components such as consensus management, real-time distributed ledgers, and identity management. This layer enhances the security and transparency of energy-related transactions and data within the digital twin framework. The results showcase a remarkable 53% reduction in peak load, emphasizing the optimized energy consumption and demand achieved. Furthermore, our platform has significantly increased the utilization of renewable energy resources by 24%, highlighting its contribution to sustainable energy resource management. Rigorous assessment of the prediction and optimization modules reveals their high accuracy and precision, with mean absolute percentage error (MAPE) values of 15.125 and 14.369, respectively. These findings underscore the efficacy and reliability of our digital twin-based approach, surpassing existing solutions and benchmarks.

Innovation in the dairy industry: forecasting cow cheese production with machine learning and deep learning models

This study focuses on the use of deep learning and machine learning models to forecast cow cheese production in Turkey. In particular, our research utilizes the LSTM (long short-term memory) model to forecast cow cheese production for the next 12 months by extensively utilizing deep learning and machine learning techniques that have not been applied in this field before. In addition to LSTM, models such as GRU (Gated Recurrent Unit), MLP (Multi-Layer Perceptron), SVR (Support Vector Regression), and KNN (K-Nearest Neighbors) were also tested, and their performances were compared using RMSE (Root Mean Square Error), MSE (Mean Squared Error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and (Coefficient of Determination) metrics. The findings revealed that the LSTM model performed significantly better than the other models in terms of RMSE, MSE, MAE, and MAPE values. This result indicates that the LSTM model provides high accuracy and reliability in forecasting cow cheese production. This achievement of the model offers important applications in areas such as supply chain management, inventory optimization, and demand forecasting in the dairy industry.

Prediksi Harga Material Bangunan Dengan Autoregressive Integrated Moving Average (Arima) Pada CV. TJA

The problem most often faced by CV TJA is that the price from the RAB (Cost Budget Plan) calculation is not in accordance with market prices, so it is necessary to predict the price of building materials to help the company prepare the RAB. The aim of this research is to examine predictions of material prices measured using the ARIMA method. The data analysis method in this study used the MA parameter (1) with the ARIMA model (0,1,1) for dynamix cement material, the MA parameter (1) ARIMA model (0,2,1) for cast sand, the MA parameter (1 ) ARIMA model (0,3,1) for threaded iron 13 and AR parameter (2) ARIMA model (2,1,0) for usuk wood material. By using 3 error testing methods, namely Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD) and Mean Squared Deviation (MSD). The results of this analysis show the lowest accuracy value, namely for dynamic cement material with a MAPE value of 5%, a MAD value of 1.986 and an MSD value is 63.584.667. The results of error tests using MAPE, MAD and MSD can be concluded that the ARIMA method is very accurate because the MAPE value is less than 10%.

The general conformable fractional grey system model and its applications

As a mathematical instrument, the fractional order grey system model can be employed to characterize uncertain real-world data. This paper presents new definitions for the generalized conformable fractional accumulation (GCFA) and the generalized conformable fractional difference (GCFD). For the first time, complex network theory explains the modelling mechanisms of accumulation. Then a novel generalized conformable fractional grey model (GCFGM(1,1)) is proposed based on the GCFA and GCFD, and the particle swarm optimizer (PSO) method is employed to optimize its fractional order. In this study, the proposed model is applied to the prediction of China’s overall energy consumption, natural gas consumption, and gross domestic product. Our research reveals that our proposed model achieves mean absolute percentage error (MAPE) values of less than 5% on real datasets from 9 different regions, outperforming other comparative models. These key empirical findings demonstrate the superiority of our model in accurately predicting relevant variables.

Machine Learning Assisted Prediction of Porosity and Related Properties Using Digital Rock Images.

Accurately estimating reservoir rock properties is paramount for modeling the storage and flow of fluids (hydrocarbon, carbon dioxide, and groundwater) in porous media. However, existing laboratory techniques to measure rock properties are usually time-consuming, expensive, and computationally intensive. This work proposes an efficient workflow that uses the machine learning algorithm, based on the convolutional neural network (CNN) framework, to predict rock properties from microcomputed tomography (micro-CT) X-ray images. The workflow involves data preprocessing, label extraction, training, and prediction using the segmented images of the rock to predict porosity, throat area, and pore surface area, which are essential for pore-scale modeling. The model was trained and validated on the Bentheimer sandstone, which was then used to predict properties of other sandstones (Castlegate and Leopard) with different pore structures and flow properties. The model yielded a good prediction for the throat and pore surface area but a significant error for porosity. Subsequently, a new complex model was trained and validated using diverse images from Bentheimer and an additional rock Castlegate, which was then used to predict the properties of Leopard sandstone. The new model improved the prediction of each property, resulting in mean absolute percentage error (MAPE) values of 2.19%, 3.04%, and 6.08% for porosity, pore surface area, and throat area with the binary images, respectively. In addition, we present a novel data-driven method using a simple regression model to predict the absolute permeability of a digital rock sample using the pore network parameters as predictors. The extreme gradient boost (XGBoost), which performed the best among several machine algorithms, was trained and validated using digital rock images from Bentheimer and Castlegate sandstone. The generated model was then used to predict the absolute permeability of the Leopard sandstone with an R 2 of 0.813, which was a significant improvement over the model generated solely by using either the Bentheimer or the Castlegate sandstone images. Furthermore, our analysis showed that the tortuosity had the most significant effect on the absolute permeability prediction of the rock sample. This study showed that we can reliably predict the morphological properties of porous media using computationally efficient models generated from digital rock images, which can be used to build a regression model to predict the crucial petrophysical properties needed to model the flow of fluids in porous media.

A quasi-static mooring analysis approach for berthed ships

The existing literature extensively explores the environmental loads experienced by moored ships, a process known as mooring analysis. In order to model this analysis realistically, it is necessary to consider a number of variables, including ships' equations of motion, response amplitude operators, and mooring equipment capabilities. However, port authorities may lack comprehensive ship data. This study introduces a quasi-static equilibrium-state search method for berthed ships. It needs just basic ship particulars data and mooring arrangement data of berth and ship. The method enables the determination of crucial information such as mooring line load distribution, rope tension, rope failure and equilibrium ship position. Its versatility across ships and ports makes it noteworthy. Various analyses conducted on the method produce results with mean absolute percentage error values ranging from 0.57% to 2.23%, which are deemed significant and meaningful. Furthermore, the mathematical model of the method, which is currently considered to have three degrees of freedom, is open to improvement. The method has the potential to contribute significantly to various aspects, including the tail effect, the pretension effect, and the analysis of ropes with different stiffness values and capabilities.

Hybrid particle swarm optimization and group method of data handling for the prediction of ultimate strength of concrete-filled steel tube columns

This study presents a hybrid model coupling particle swarm optimization (PSO) with group method of data handling (GMDH) for predicting the ultimate strength of rectangular concrete-filled steel tube (RCFST) columns. A large database of 490 data samples collected from the existing literature was used to construct the model. Compared with the optimal model among the nine existing models, the coefficient of variation (COV), mean absolute percentage error (MAPE) and root relative squared error (RRSE) values of all datasets of the PSO-GMDH model were decreased by 58.38 %, 69.22 % and 64.27 %, respectively; while the coefficient of determination (R2) and a20-index values were increased by 34.32 % and 8.65 %, respectively. The results show that the predicted results of PSO-GMDH model are in good agreement with the experimental results and can accurately predict the ultimate strength of rectangular RCFST columns. In addition, a graphical user interface (GUI) has been developed to facilitate the application of the PSO-GMDH model.

Leveraging MPPT capability for solar irradiance estimation: H-INC-IBS-based assessment of explicit models under real-world climatic conditions

This research investigates a low-cost method for estimating irradiance. The approach uses maximum power point tracking (MPPT) to easily estimate irradiance in a Photovoltaic (PV) system. Two main explicit mathematical models "IV-G" and "I-G" are examined for effective irradiance estimation. Given the requirement for MPPT, the examined PV system is powered by a hybrid-incremental conductance integral backstepping controller (H-INC-IBS), which enables proper maximum power point functioning. These models are evaluated using statistical indices such as the root mean square error (RMSE), mean absolute percentage error (MAPE), symmetrical mean absolute percentage error (SMAPE), and coefficient of correlation (R). The research tests these models using 37 climate patterns, demonstrating the influence of MPPT on model performance. In addition, the models are closely monitored in an experimental setup that includes two consecutive days of real-time climatic exposure. The experimental results show that the I-G model has the best accuracy measures, with worst MAE, MAPE, and SMAPE values of 18.81 W, 8.9039 %, and 4.7242 %, respectively. Thus, the article concludes that the I-G model is better suited for irradiance estimate in the context of MPPT. Furthermore, using evidence-based study outcomes, the paper proves that MPPT accuracy is proportional to estimating model accuracy. Given the little attention paid to this area of irradiance estimation, this work is intended to serve as a reference in the field.