Mean Square Error Values Research Articles

Advancing Mix Design Prediction in 3D Printed Concrete: Predicting Anisotropic Compressive Strength and Slump Flow

Introducing 3D-concrete printing has started a revolution in the construction industry, presenting unique opportunities alongside undeniable challenges. Among these, the major challenge is the iterative process associated with mix design formulation, which results in significant material and time consumption. This research uses machine learning (ML) techniques such as Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Decision Tree Regression (DTR), Gaussian Process Regression (GPR), and Artificial Neural Network (ANN) to overcome these challenges. A dataset containing 21 mix constituent features and 4 output properties (cast and printed compressive strength, and slump flow) was extracted from the literature to investigate the relationship between mix design and performance. The models were assessed using a range of evaluation metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R-squared value. Gaussian Process Regression (GPR) yielded more favorable results. In the case of cast strength, GPR achieved an R2 value of 0.9069, along with RMSE, MSE, and MAE values of 13.04, 170.12, and 9.40, respectively. A similar trend was observed for printed strengths in directions 1, 2, and 3. GPR achieved R-squared values exceeding 0.91 for all directions, accompanied by significantly lower RMSE values (below 4.1). The machine learning models were also validated using four unique mix designs. These mixes were 3D printed and tested for compressive strength and slump flow. GPR's average error was 10.55%, while SVM achieved a slightly lower average error of 9.38%. Overall, this work presents a novel approach for optimizing 3D-printed concrete by enabling the prediction of slump flow and compressive strength directly from the mix design. This approach can facilitate the design and fabrication of 3D-printed concrete structures that fulfill the necessary strength and printability requirements.

Optimizing food waste anaerobic digestion in Kuwait: Experimental insights and empirical modelling using artificial neural networks.

This study investigates, for the first time, the anaerobic digestion of food waste in Kuwait to optimize methane production through a combination of artificial neural network (ANN) modelling and continuous reactor experiments. The ANN model, utilizing eight hidden neurons and a 70-20-10 split for training, validation and testing sets, yielded mean squared error values of 0.0056, 0.0048 and 0.0059 and coefficient of determination (R²) values of 0.9942, 0.9986 and 0.9892, respectively. Methane percentages in biogas were predicted using six parameters: biomass type, pH, organic loading rate (OLR), hydraulic retention time (HRT), temperature and reactor volume. To validate the ANN results, continuous reactor experiments were conducted under an OLR of 3 kg VS m⁻³ d⁻¹ and HRT of 20 days at varying temperatures (35°C, 40°C, 45°C, 50°C and 55°C). The experiments demonstrated optimal methane production in the mesophilic range, with ANN predictions closely aligning with experimental data up to 45°C. However, deviations were observed at higher temperatures, particularly under thermophilic conditions beyond 50°C. This study provides novel insights into waste-to-energy initiatives in Kuwait and highlights the potential of integrating computational models with empirical data to enhance biogas production processes.

Analisis Peramalan Penjualan untuk Perencanaan Produksi pada Lavanda Brownies Karawang

This research was conducted at Lavanda Brownies Karawang which compared the moving average and exponential smoothing forecasting methods. This research aims to find out, calculate, analyze and explain how to use the moving average and exponential smoothing methods in sales forecasting for production planning at Lavanda Brownies Karawang as well as to find out, calculate, analyze and explain what forecasting method is most appropriate to use for Lavanda Brownies Karawang based on calculations of Mean Absolute Devitiation (MAD), Mean Square Error (MSE), and Mean Absolute Percent Error (MAPE). This research uses a quantitative descriptive method, to get a general picture of the company's condition, a literature review related to the research topic is carried out as well as field research through interviews and direct observation. Based on the research results, the exponential smoothing forecasting method based on alpha 0.9 is the best forecasting method for production planning. This method was chosen because it has the smallest MAD, MSE and MAPE accuracy values ​​when compared to other methods, namely with MAD of 489,909, MSE of 506.950 and a MAPE percentage of 14,14%, which percentage has the smallest value and is included in the category Good. A smaller MAPE accuracy value indicates more precise forecasting. Because in forecasting the smaller the accuracy value, the better the forecasting results so that the Lavanda Brownies business can plan optimal steamed brownie production activities in the next period.

Analyzing Cancer Survival Times Using the Exponentiated Fréchet-Gompertz Distribution

Understanding the survival of cancer patients is essential for determining optimal treatment strategies. This research introduces a robust distribution for analyzing survival data known as the Exponential Fr ́echet-Gompertz distribution (EFG). The EFG combines the unique characteristics of the Exponential Fr ́echet and Gompertz distributions, enhancing its efficiency and providing greater flexibility in representing complex datasets. The study specifically investigates the EFG distribution’s effectiveness in modeling cancer patients’ survival times. A comprehensive analysis of the distribution’s properties is presented, including the quantile and quartile functions, shape indices, moments, moment-generating function, characteristic function, mean residual life, mean waiting time, R ́enyi entropy, and order statistics. The parameters of the distribution are derivedusing five distinct methods: Maximum Likelihood Estimation (MLE), Ordinary Least Squares (OLS), Weighted Least Squares (WLS), Cram ́er-von Mises (CVM), and Maximum Product of Spacings (MPS). A Monte Carlo simulation technique is employed to evaluate the performance of these estimation methods. The simulation results indicate that as sample size increases, the meansquare error (MSE) values for all estimators decrease. Notably, the MLE exhibits the lowest MSE, while the MPS has the highest MSE, particularly for smaller sample sizes. Furthermore, the study presents a comprehensive comparison of the effectiveness of these estimation methods in analyzing survival times for various cancer types, including bladder, bone, blood and brain cancer. The results indicate that the EFG distribution is an optimal model for representing the survival times of patients across these different cancers data. Furthermore, the W.LS method yielded superior estimations for most datasets concerning cancer patient survival. Overall, the EFG distribution demonstrates exceptional capability in accurately fitting survival times for cancer patients compared to other competing distributions.

COMPARISON OF NONLINEAR MODELS FOR DESCRIBING THE GROWTH OF LOCAL CHICKENS RAISED IN TOGO

Growth is a trait of economic importance in chicken genetic improvement that is often described by mathematical models having biologically significant parameters. The local chicken,commonly raised in rural areas of Togo, is generally described as a slow-growing chicken, but little is known about his growth parameters. Therefore, this study was designed to determine the nonlinear model that best describes the growth curve of local chickens. The Gompertz, Logistic and Richards growth models were used to fit growth weights recorded from 126 males and 150 female chickens using GCFIT program of SIMFIT package (version 8.1.4).Coefficient of determination (R2), mean square error (MSE), Akaikes information criterion (AIC) and Bayesian information criterion (BIC) were used as criteria for models comparison. By estimating the highest value of R² and the lowest values of MSE, AIC and BIC, Gompertz model appeared to be the best-fitting model for local chickensgrowth. The estimated asymptotic weight was 1992 g for males and 1367 g for females. Thus, male and female chickens weighed 732.86g and 502.98g and aged 10.57 weeks and 9.77 weeks respectively at inflection point. Maturation rates of the chicks were similar (0.139 week-1 for males and 0.141 week-1for females). The growth parameters of this model may therefore be useful in defining selection criteria for local chickens in Togo.

Biological activities of Hypericum spectabile extract optimized using artificial neural network combined with genetic algorithm application

Optimizing extraction conditions can help maximize the efficiency and yield of the extraction process while minimizing negative impacts on the environment and human health. For the purpose of the current study, an artificial neural network (ANN) combined with a genetic algorithm (GA) was utilized for that the extraction conditions of Hypericum spectabile were optimized. In this particular investigation, the main objective was to get the highest possible levels of total antioxidant status (TAS) for the extracts that were obtained. In addition to this, conditions of the extract that exhibited the maximum activity have been determined and the biological activity of the extract that was obtained under these conditions was analyzed. TAS values were obtained from extracts obtained using extraction temperatures of 30–60 °C, extraction times of 4–10 h, and extract concentrations of 0.25-2 mg/mL. The best model selected from the established ANN models had a mean absolute percentage error (MAPE) value of 0.643%, a mean squared error (MSE) value of 0.004, and a correlation coefficient (R) value of 0.996, respectively. The genetic algorithm proposed optimal extraction conditions of an extraction temperature of 59.391 °C, an extraction time of 8.841 h, and an extraction concentration of 1.951 mg/mL. It was concluded that the integration of ANN-GA can successfully be used to optimize extraction parameters of Hypericum spectabile. The total antioxidant value of the extract obtained under optimum conditions was determined as 9.306 ± 0.080 mmol/L, total oxidant value as 13.065 ± 0.112 µmol/L, oxidative stress index as 0.140 ± 0.001. Total phenolic content (TPC) was 109.34 ± 1.29 mg/g, total flavonoid content (TFC) was measured as 148.34 ± 1.48 mg/g. Anti-AChE value was determined as 30.68 ± 0.77 µg/mL, anti-BChE value was determined as 41.30 ± 0.48 µg/mL. It was also observed that the extract exhibited strong antiproliferative activities depending on the increase in concentration. As a result of LC-MS/MS analysis of the extract produced under optimum conditions in terms of phenolic content. The presence of fumaric, gallic, protocatechuic, 4-hydroxybenzoic, caffeic, 2-hydoxycinamic acids, quercetin and kaempferol was detected. As a result, it was determined that the H. spectabile extract produced under optimum conditions had significant effects in terms of biological activity.

Machine Learning Models Informed by Connected Mixture Components for Short- and Medium-Term Time Series Forecasting

This paper presents a new approach in the field of probability-informed machine learning (ML). It implies improving the results of ML algorithms and neural networks (NNs) by using probability models as a source of additional features in situations where it is impossible to increase the training datasets for various reasons. We introduce connected mixture components as a source of additional information that can be extracted from a mathematical model. These components are formed using probability mixture models and a special algorithm for merging parameters in the sliding window mode. This approach has been proven effective when applied to real-world time series data for short- and medium-term forecasting. In all cases, the models informed by the connected mixture components showed better results than those that did not use them, although different informed models may be effective for various datasets. The fundamental novelty of the research lies both in a new mathematical approach to informing ML models and in the demonstrated increase in forecasting accuracy in various applications. For geophysical spatiotemporal data, the decrease in Root Mean Square Error (RMSE) was up to 27.7%, and the reduction in Mean Absolute Percentage Error (MAPE) was up to 45.7% compared with ML models without probability informing. The best metrics values were obtained by an informed ensemble architecture that fuses the results of a Long Short-Term Memory (LSTM) network and a transformer. The Mean Squared Error (MSE) for the electricity transformer oil temperature from the ETDataset had improved by up to 10.0% compared with vanilla methods. The best MSE value was obtained by informed random forest. The introduced probability-informed approach allows us to outperform the results of both transformer NN architectures and classical statistical and machine learning methods.

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

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

Predicting physicochemical properties of papayas (Carica papaya L.) using a convolutional neural networks model approach.

The current state of quality assessment methods for agricultural produce, particularly fruits, heavily relies on manual inspection techniques, which could be subjective, time-consuming, and prone to human errors. Consequently, there have been emerging trends and needs for non-destructive methods to evaluate fruit quality accurately and practically. This research aimed to develop a novel approach for predicting the physicochemical properties of papayas using a convolutional neural network (CNN) model that combines image analysis and weight assessment. This study involved capturing images of papayas at different ripening stages, measuring papaya weights, and determining various physicochemical properties such as texture, pH, total soluble solids, and seed weight. A total of 532 images were obtained from 132 papayas, and an additional 1064 images were generated through image augmentation. The dataset was divided into three sets with an 8:1:1 ratio for training, validation, and testing. The CNN model was trained using papaya images and weights as input values to predict and estimate the physicochemical property values. Model performance was evaluated using mean squared error (MSE) and the coefficient of determination (R2) as metrics. The CNN model, integrated with image processing, could predict the diverse physicochemical properties of papayas with high accuracy. The MSE values estimated for the training and validation sets were 0.0284 and 0.1729, respectively. The R2 values for the test dataset ranged from 0.71 to 0.94. These findings demonstrate that CNN-based models could provide detailed and quantitative insights, facilitating improved understanding and management of papaya quality while enhancing predictive modeling accuracy in agriculture. PRACTICAL APPLICATION: This research introduces a new method for accurately predicting the quality of papayas using a computer model. Instead of relying on manual inspection, which can be slow and prone to errors, this model uses images of papayas and their weights to predict properties, including texture, pH, total soluble solids, and seed weight. This can help manage papaya quality better while also improving agricultural production and transportation processes.

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

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

Ionospheric TEC and its irregularities over Egypt: a comprehensive study of spatial and temporal variations using GOCE satellite data

Abstract This study delves into ionospheric characteristics during solar cycle 24 using data from the Global Positioning System (GPS) and Low Earth Orbit (LEO) satellites, GOCE. The present study fills the gap of not assessing and studying GOCE satellite data before in Egypt. Focusing on spatial and temporal variations of ionospheric Total Electron Content (TEC) over Egypt and the Middle East across a 4-year dataset, monthly average Vertical Total Electron Content (VTEC) variations are scrutinized, emphasizing extremes during heightened and reduced solar and geomagnetic activities. Results TEC typically decreases with latitude’s increase, with peak ionization during equinoxes and troughs during solstices. Notably, VTEC values consistently reach maximum values on days of heightened solar and geomagnetic activities. GOCE data is evaluated against International Reference Ionosphere (IRI) model and NeQuick2 model by selecting 10 days from all data by using a statistical comparison via t-tests. There is not significant difference between them except for two days between GOCE-IRI. The values of Root Mean Square Error (RMSE) are 3.7403 and 4.4655 for GOCE – NeQuick2 model and GOCE – IRI model, respectively. Ionospheric scintillation, signifying rapid electron density fluctuations, is assessed through amplitude scintillation index (S4), S4 proxy, and Rate Of TEC Index (ROTI). A robust correlation between S4 and S4 proxy is noted, thus scintillation can be studied by using S4 proxy instead of S4. Temporal variations indicate heightened scintillation during geomagnetic storms and peak solar activity, contrasting with reduced activity during solar minimum. Throughout the study period the maximum scintillation index values for ROTI, S4, and S4 proxy are 0.3, 0.15, and 0.05, indicating minimal scintillation. This comprehensive analysis, rooted in GOCE data, illuminates spatial and temporal dynamics of ionospheric TEC and elucidates ionospheric scintillation characteristics in the Egypt region. These findings are crucial for refining ionospheric models, improving scintillation prediction, and enhancing satellite communication and navigation systems, especially in the study region.

NIR Spectroscopy for the Online Monitoring of Water and Olive Oil Content in Pomace during the Extraction Process

The main challenge of this scientific work was the implementation on an industrial olive oil extraction plant of an NIR device for the multispectral analysis of pomace to predict the percentage of humidity and oil contained in it. Subsequent to the implementation of the NIR device on the oil extraction line on the solid’s outlet from the decanter, NIRS interaction measurements in the 761–1081 nm region were used to probe the pomace. NIRS calibration models for the prediction of water and oil content in the pomace were obtained and successfully tested and validated. The correlations of calibration results for oil and water content were 0.700 and 0.829, while the correlations of validation were 0.773 and 0.676, respectively. Low values of root mean square error were found for both the prediction and validation set. The results highlight the good robustness of an NIR approach based on a PLS calibration model to monitor the industrial olive oil process. The results obtained are a first step toward the large-scale implementation of NIR devices for monitoring pomace in oil mills. The possibility of knowing the oil lost in the pomace, moment by moment, would open a new frontier towards system control and the sustainability of the olive oil extraction process.

Exploiting the synergy of SARIMA and XGBoost for spatiotemporal earthquake time series forecasting

AbstractEarthquakes are vibrations that occur on the surface of earth, generating fires, ground shaking, tsunamis, landslides and cracks. These incidents can cause severe damage and loss of life. Accurate earthquake forecasts are critical for anticipating and mitigating these hazards, which can avoid damage to buildings and infrastructure and save lives. To address the challenges given by earthquakes probabilistic nature, this paper presents a hybrid SARIMA–XGBoost approach to earthquake magnitude prediction. The suggested technique consists of a two‐step process: an exploration phase that uses exploratory data analysis, which includes descriptive statistics and data visualisation, and a prediction phase that focusses on forecasting future earthquakes. Using a large significant earthquake dataset spanning 1965–2023, the study intends to gain insights and lessons for more effective earthquake prediction methods. Further, in a comparison analysis, the results of SARIMA‐XGBoost model are compared to those of traditional ARIMA and SARIMA models. The results highlight the superior performance of the hybrid SARIMA–XGBoost model, showcasing a mean absolute error (MAE) of 0.038, a mean squared error (MSE) of 0.0040, and a root mean squared error (RMSE) of 0.068. These metrics collectively underscore the model's enhanced accuracy in forecasting earthquake magnitudes. The notably low values of MAE, MSE and RMSE indicate that our hybrid approach significantly improves prediction accuracy compared to alternative models. By integrating SARIMA's time series (TS) analysis with XGBoost's machine learning (ML) capabilities, the hybrid model reduces forecasting errors more effectively, demonstrating its clear advantage in precision.

Evaluation of the Performance of Kernel Non-parametric Regression and Ordinary Least Squares Regression

Researchers need to understand the differences between parametric and nonparametric regression models and how they work with available information about the relationship between response and explanatory variables and the distribution of random errors. This paper proposes a new nonparametric regression function for the kernel and employs it with the Nadaraya-Watson kernel estimator method and the Gaussian kernel function. The proposed kernel function (AMS) is then compared to the Gaussian kernel and the traditional parametric method, the ordinary least squares method (OLS). The objective of this study is to examine the effectiveness of nonparametric regression and identify the best-performing model when employing the Nadaraya-Watson kernel estimator method with the proposed kernel function (AMS), the Gaussian kernel, and the ordinary least squares (OLS) method. Additionally, it determines which method yields the most accurate results when analyzing nonparametric regression models and provides valuable insights for practitioners looking to apply these techniques in real-world scenarios. However, criteria such as generalized cross-validation (GCV), mean square error (MSE), and coefficient determination are used to select the most efficient estimated model. Simulated data was used to evaluate the performance and efficiency of estimators using different sample sizes. The results favorable the simulation illustrate that the Nadaraya-Watson kernel estimator using the proposed kernel function (AMS) exhibited favorable and superior performance compared to other methods. The coefficients of determination indicate that the highest values attained were 98%, 99%, and 99%. The proposed function (AMS) yielded the lowest MSE and GCV values across all samples. Therefore, this suggests that the model can generate precise predictions and enhance the performance of the focused data.

Improve Metode Lightgbm untuk Prediksi Harga Mobil Bekas Menggunakan Hyper-Parameter Tuning

This study aims to predict used car prices using the LightGBM method and hyperparameter tuning techniques in the context of data science. The analysis process includes collecting historical data on used cars, preprocessing the data to clean and encode variables, and splitting the data into training and testing sets. The LightGBM model was trained and optimized through hyperparameter tuning using GridSearchCV to improve model performance. The model was evaluated using metrics such as Mean Squared Error (MSE) and R-squared. The results indicate that the well-optimized LightGBM model can accurately predict used car prices with high accuracy. The low MSE value (35207938112.028404) and high R-squared value (0.9462871489515565) demonstrate the model's excellent predictive quality. This research provides deeper insights into the factors influencing used car prices and contributes to the development of effective and reliable predictive models.

Effects of water quality and nitrogen on wheat productivity: Experimental and modelling study using the SALTMED model

AbstractThe effects of different water qualities and nitrogen doses need to be investigated for wheat growth to determine the optimal management strategy for sustaining wheat production potential to ensure human food security. Therefore, a 2‐year study (2020–2021 to 2021–2022) was conducted on wheat irrigated with different water qualities, including canal water (Ca), tubewell water (Tu) and mixed Ca‐Tu water (Mx), each fertilized with two nitrogen doses, that is, N75 = 75 kgN.ha−1 and N100 = 100 kgN.ha−1. Ca⨯N100 performed best, with 5.12 t.ha−1 grain yield, 11.60 t.ha−1 biomass, 102.83 cm plant height and 1.32 kg.m−3 water productivity. The best values of the root mean square error (RMSE), normalized root mean square error (NRMSE), coefficient of determination (R2) and coefficient of residual mass (CRM) were 0.11, 0.12, 0.93 and −0.004, respectively, for calibration and 0.13, 0.15, 0.87 and ±0.01, respectively, for validation of the SALTMED model. The scenario simulation was performed for additional levels of water salinity (electrical conductivity [EC] = 8 and 12 dS.m−1) and nitrogen doses (50, 125, 150 and 175 kgN.ha−1). The results revealed improvements in wheat grain yield of 107%, 16% and −6% at EC = 8 dS.m−1 and 125%, 31% and 5% at EC = 12 dS.m−1, while the improvements in biomass were 113%, 22% and −2% at EC = 8 dS.m−1 and 137%, 29% and 8% at EC = 12 dS.m−1 with increasing nitrogen doses from 50–125 kg.ha−1, 125–150 kg.ha−1 and 150–175 kg.ha−1, respectively. It is recommended that high‐quality water with the lowest possible EC and nitrogen applications of up to 150 kgN.ha−1 be adopted for wheat production in semi‐arid areas of Punjab, Pakistan.

Study on Fast Temporal Prediction Method of Flame Propagation Velocity in Methane Gas Deflagration Experiment Based on Neural Network

To address the challenges of high experimental costs, complexity, and time consumption associated with pre-mixed combustible gas deflagration experiments under semi-open space obstacle conditions, a rapid temporal prediction method for flame propagation velocity based on Ranger-GRU neural networks is proposed. The deflagration experiment data are employed as the training dataset for the neural network, with the coefficient of determination (R2) and mean squared error (MSE) used as evaluation metrics to assess the predictive performance of the network. First, 108 sets of pre-mixed methane gas deflagration experiments were conducted, varying obstacle parameters to investigate methane deflagration mechanisms under different conditions. The experimental results demonstrate that obstacle-to-ignition source distance, obstacle shape, obstacle length, obstacle quantity, and thick and fine wire mesh obstacles all significantly influence flame propagation velocity. Subsequently, the GRU neural network was trained, and different activation functions (Sigmoid, Relu, PReLU) and optimizers (Lookahead, RAdam, Adam, Ranger) were incorporated into the backpropagation updating process of the network. The training results show that the Ranger-GRU neural network based on the PReLU activation function achieves the highest mean R2 value of 0.96 and the lowest mean MSE value of 7.16759. Therefore, the Ranger-GRU neural network with PReLU activation function can be a viable rapid prediction method for flame propagation velocity in pre-mixed methane gas deflagration experiments under semi-open space obstacle conditions.

Adjustment of the Height Triangular Fuzzy Regression as Early Awareness of Breast Cancer

Breast cancer is the most popular malignancy among Malaysian women. In Malaysia, approximately one in every 19 women is at risk. Breast cancer is the most common cancer among Malaysian women (32.9%), followed by colorectal (11.9%) and ovarian cancers (7.2%). Many cases are found to be advanced, with considerable tumour development or metastasis to untreatable locations and unaware of the factors. This study aims to determine the best height triangular fuzzy regression model by adjustment between 0 – 1 and measure the value of statistical error using mean square error (MSE) and root mean square error (RMSE). Secondary data was used where 569 patients having BREAST cancer and receiving treatment in hospitals was recorded by nurses and doctors. The patient data for breast cancer were analysed using MATLAB, SPSS, and Microsoft Excel. Based on the results, H = 0 of fuzzy linear regression is the best model to predict the breast cancer awareness with lowest value of MSE and RMSE by 1.455 and 1.206 respectively. Malaysians must be aware of the warning factors of breast cancer to increase survival and minimise death rates.

Assessment of CROPWAT 8.0 model accuracy under deficit irrigation scheduling: A sustainable path toward smart water supply

ABSTRACT As the world confronts mounting challenges to water security, efficient irrigation becomes ever more critical. Escalating water scarcity challenges necessitate innovative smart irrigation solutions. This study investigates the potential of integrating deficit irrigation (DI) with the CROPWAT 8.0 model for sustainable and water-productive irrigation. This research evaluated the model's accuracy in generating DI schedules at 70% field capacity (FC) (T1) compared to schedules derived from frequency domain reflectometry (FDR) soil moisture sensors at 70% FC (T2). The results demonstrated economic viability and strong accuracy for T1 with consistently lower values of mean absolute error (3.6165 and 4.3987) and root mean square error (4.4447 and 5.5055) while compared to T2 across two years. In addition, T1 achieved significant water savings (43.47 and 45.16%) alongside higher water productivity (8.11 and 8.22 kg m−3), respectively, for both the years compared to full irrigation (FI). These findings highlight CROPWAT 8.0 model–driven DI scheduling as a promising smart irrigation approach for tackling water scarcity, offering economic benefits to farmers while promoting water security.

An Artificial Neural Network approach to assess road roughness using smartphone-based crowdsourcing data

Monitoring road surface conditions is a crucial task for road authorities to develop effective infrastructure maintenance programs. Despite smartphones have been introduced as cost-effective and real-time solution for this purpose, several challenges must be addressed before their real-world application. This study investigates the utilization of smartphone-based crowdsourcing data and Artificial Neural Networks (ANN) to enhance the precision of road surface condition estimation. Initially, data are collected from four different smartphone models mounted in various vehicles, including vertical acceleration, geographic location, and speed. The root mean square of the vertical acceleration data, along with vehicle speed, is then employed as input features for the ANN, while the true International Roughness Index (IRI) values serve as the corresponding output features. Comparative analysis between ANN and regression models based on statistical metrics such as Mean Squared Error (MSE) and Pearson correlation revealed that ANN outperforms regression models. The obtained MSE and Pearson correlation values for ANN (0.56 and 0.91) surpass those of regression models (0.72 and 0.88). Moreover, results indicated that utilizing crowdsourcing smartphone data yielded superior outcomes compared to using a single smartphone for this purpose.