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Plastic pollution, driven by micro- and nanoplastics (MNPs), poses a major environmental threat, exposing humans through various routes. Despite human colorectal adenocarcinoma Caco-2 cells being used as an in vitro model for studying the intestinal epithelium, uncertainties linger about MNPs harming these cells and the factors influencing adverse effects. Addressing this lacuna, our study aimed to elucidate the pivotal MNP parameters influencing cytotoxicity in Caco-2 cells, employing meta-analysis and machine learning techniques for quantitative assessment. Initial scrutiny of 95 publications yielded 17 that met the inclusion criteria, generating a dataset of 320 data points. This dataset underwent meticulous stratification based on polymer type, exposure time, polymer size, MNP concentration, and biological assays utilised. Subsequent dose-response curve analysis revealed moderate correlations for selected subgroups, such as the (3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide) MTT biological assay and exposure time exceeding 24 h, with coefficient of determination (R 2 ) values of 0.50 (p-value: 0.0065) and 0.60 (p-value: 0.0018) respectively. For the aforementioned two subgroups, the MNP concentrations surpassing 10 μg/mL led to diminished viability of Caco-2 cells. Notably, we observed challenges in employing meta-analysis to navigate this multidimensional MNP dataset. Leveraging a random forest model, we achieved improved predictive performance, with R 2 values of 0.79 and a root mean square error (RMSE) of 0.14 for the prediction of the Log Response Ratio on the test set. Model interpretation indicated that size and concentration are the principal drivers influencing Caco-2 cell cytotoxicity. Additionally, the partial dependence plot illustrating the relationship between the size of MNPs and predicted cytotoxicity reveals a complex pattern. Our study provides crucial insights into the health impacts of plastic pollution, informing policymakers for targeted interventions, thus contributing to a comprehensive understanding of its human health consequences., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Eucalyptus is a widely planted species in plantation forests because of its outstanding characteristics, such as fast growth rate and high adaptability. Accurate and rapid prediction of Eucalyptus biomass is important for plantation forest management and the prediction of carbon stock in terrestrial ecosystems. In this study, the performance of predictive biomass regression equations and machine learning algorithms, including multivariate linear stepwise regression (MLSR), support vector machine regression (SVR), and k-nearest neighbor (KNN) for constructing a predictive forest AGB model was analyzed and compared at individual tree and stand scales based on forest parameters extracted by Unmanned Aerial Vehicle-Light Detection and Ranging (UAV LiDAR) and variables screened by variable projection importance analysis to select the best prediction method. The results of the study concluded that the prediction model accuracy of the natural transformed regression equations (R 2 = 0.873, RMSE = 0.312 t/ha, RRMSE = 0.0091) outperformed that of the machine learning algorithms at the individual tree scale. Among the machine learning models, the SVR prediction model accuracy was the best (R 2 = 0.868, RMSE = 7.932 t/ha, RRMSE = 0.231). In this study, UAV-LiDAR-based data had great potential in predicting the AGB of Eucalyptus trees, and the tree height parameter had the strongest correlation with AGB. In summary, the combination of UAV LiDAR data and machine learning algorithms to construct a predictive forest AGB model has high accuracy and provides a solution for carbon stock assessment and forest ecosystem assessment.

Determining nucleic acid concentrations in a sample is an important step prior to proceeding with downstream analysis in molecular diagnostics. Given the need for testing DNA amounts and its purity in many samples, including in samples with very small input DNA, there is utility of novel machine learning approaches for accurate and high-throughput DNA quantification. Here, we demonstrated the ability of a neural network to predict DNA amounts coupled to paramagnetic beads. To this end, a custom-made microfluidic chip is applied to detect DNA molecules bound to beads by measuring the impedance peak response (IPR) at multiple frequencies. We leveraged electrical measurements including the frequency and imaginary and real parts of the peak intensity within a microfluidic channel as the input of deep learning models to predict DNA concentration. Specifically, 10 different deep learning architectures are examined. The results of the proposed regression model indicate that an R_Squared of 97% with a slope of 0.68 is achievable. Consequently, machine learning models can be a suitable, fast, and accurate method to measure nucleic acid concentration in a sample. The results presented in this study demonstrate the ability of the proposed neural network to use the information embedded in raw impedance data to predict the amount of DNA concentration.

This research presents a machine learning (ML)‐based model that determines the DC and RF characteristics of InGaAs sub‐channel double gate high electron mobility transistors (DG‐HEMTs) to optimize the device structure. We employ technology computer‐aided design (TCAD) simulations to analyze the DC and RF performance of InGaAs sub‐channel DG‐HEMTs, generating a range of datasets by varying the material composition, layer width, and thickness of different layers in the device structure. We then train and optimize support vector regression (SVR) models using 5‐fold cross‐validation, varying the kernel function and degree parameters, and achieve better performance with the radial basis function (RBF) kernel. The simulated results indicate that the ML model predicts physical parameters more effectively than experimental analysis, offering a compact modeling solution that requires fewer computing resources than traditional methods. [ABSTRACT FROM AUTHOR]

Educational data mining (EDM) is a strategic technique for exploring data in educational environments to gain a deeper understanding of education. One of the goals of EDM is to predict things related to students in the future which can be done using a machine learning approach. In this paper, a regression model is developed to predict student performance in the first semester and the waiting period for graduate employment using machine learning approach based on informatics management (MI) and noninformatics management (non-MI) student data. Four regression models are compared for predicting student performance in the first semester and waiting period for graduate employment, including support vector regression (SVR), random forest regression (RFR), AdaBoost regression (ABR), and XGBoost regression. Based on the experiment, prediction of students' performance in the first semester, the highest R2 result produced by SVR model by value of 0.58 for MI and by RFR by value of 0.34 for non-MI. While, waiting period for graduate employment prediction, the highest R2 result produced by AdaBoost regression by value of 0.44 for MI and SVR by value of 0.39 for non-MI. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative method for enhancing time series data preprocessing by integrating a cycling layer into a self-attention mechanism. Traditional approaches often fail to capture the cyclical patterns inherent to time series data, which affects the predictive model accuracy. The proposed method aims to improve models' ability to identify and leverage these cyclical patterns, as demonstrated using the Jena Climate dataset from the Max Planck Institute for Biogeochemistry. Empirical results show that the proposed method enhances forecast accuracy and speeds up model fitting compared to the conventional techniques. This paper contributes to the field of time series analysis by providing a more effective preprocessing approach. [ABSTRACT FROM AUTHOR]

Background: An abnormal variation in blood electrolytes, such as potassium, contributes to mortality in children admitted to intensive care units. Continuous and real-time monitoring of potassium serum levels can prevent fatal arrhythmias, but this is not currently practical. The study aims to use machine learning to estimate blood potassium levels with accuracy in real time noninvasively. Methods: Hospitalized patients in the Pediatric Department of the Rajaie Cardiology and Medical Research Center and Tehran Heart Center were recruited from December 2021 to June 2022. The electrocardiographic (ECG) features of patients were evaluated. We defined 16 features for each signal and extracted them automatically. The dimension reduction operation was performed with the assistance of the correlation matrix. Linear regression, polynomials, decision trees, random forests, and support vector machine algorithms have been used to find the relationship between characteristics and serum potassium levels. Finally, we used a scatter plot and mean square error (MSE) to display the results. Results: Of 463 patients (mean age: 8 ± 1 year; 56% boys) hospitalized, 428 patients met the inclusion criteria, with 35 patients having a high noise of ECG were excluded. After the dimension reduction step, 11 features were selected from each cardiac signal. The random forest regression algorithm showed the best performance with an MSE of 0.3. Conclusion: The accurate estimation of serum potassium levels based on ECG signals is possible using machine learning algorithms. This can be potentially useful in predicting serum potassium levels in specific clinical scenarios.

The determination of geotechnical correlations and coefficients relies on the assumption of error-free training data used for empirical models, but this assumption may not always hold true. Empirical-risk assessments based on noisy data cannot guarantee the accuracy of regression results. The statistical robustness of empirical-design parameters is influenced both by the soil properties and regression models used. This study proposed a non-stochastic regression method for predicting the hydraulic conductivity of sandy soils based on relevant soil parameters. No changes in content have been made. The approach involved the following steps: data preprocessing, regression-algorithm selection, model optimization, uncertainty estimation, and model selection. The study identified trends in hydraulic conductivity and pore-structure characteristics based on specific model parameters that were derived from empirical data. The paper presents a compilation of a regression model and methods for fine-tuning parametric representations. The prediction results highlighted the best-fitting model and parameter combination with the lowest residuals, comparing favorably to empirical regression models. Machine-learning-based regression models suggest an optimal combination of properties while considering model performance and handling missing values for uncovering the relationships between hydraulic conductivity and multiple, influential, soil properties. [ABSTRACT FROM AUTHOR]

In the dynamic landscape of the automotive industry, innovation is the key driver of success. Companies that actively engage in idea generation and have efficient idea management systems in place often gain a competitive edge. This article explores the importance and necessity of idea generation in the context of the automotive industry in Romania, shedding light on the economic effects it can have. Based on a review of scientific literature, the paperwork adds value by filling in a gap on continuous improvement process, idea management, and idea management system concepts, by describing in detail how Idea Management System can be introduced, and by performing a quantitative analysis, using complex statistical methods and machine learning on big data collected to observe the effects of Idea Management System use on the results and the level of employee involvement, as well as finding out a predictor of potential savings for automotive company. Very few academic papers take into consideration the economic effect of Idea Management System especially for the company' performance (KPIs), focusing rather on performance metrics for idea management. [ABSTRACT FROM AUTHOR]

Existing sales forecasting models are not comprehensive and flexible enough to consider dynamic changes and nonlinearities in sales time-series at the store and product levels. To capture different big data characteristics in sales forecasting data, such as seasonal and trend variations, this study develops a hybrid model combining adaptive trend estimated series (ATES) with a deep neural network model. ATES is first used to model seasonal effects and incorporate holiday, weekend, and marketing effects on sales. The deep neural network model is then proposed to model residuals by capturing complex high-level spatiotemporal features from the data. The proposed hybrid model is equipped with a feature-extraction component that automatically detects the patterns and trends in time-series, which makes the forecasting model robust against noise and time-series length. To validate the proposed hybrid model, a large volume of sales data is processed with a three-dimensional data model to effectively support business decisions at the product-specific store level. To demonstrate the effectiveness of the proposed model, a comparative analysis is performed with several state-of-the-art sales forecasting methods. Here, we show that the proposed hybrid model outperforms existing models for forecasting horizons ranging from one to 12 months. [ABSTRACT FROM AUTHOR]

Rice has been one of the most important crops in the world. Among them, the rapid on-site testing of husked rice yield and moisture content can greatly facilitate to acquisition and grade in the paddy field, thereby reducing the losses during transportation, storage, and processing. This study aims to identify the optimal model for the prediction of the moisture content and husked rice yield of paddy. An LCR meter was used to test six electrical parameters (dielectric constant, capacitance, dielectric loss, dielectric loss tangent, conductivity, and electrical conductivity) of paddy samples with the moisture content ranging from 9.94% to 23.51% at frequencies between 1 and 8 MHz. Subsequently, a rice huller was used to dehusk the paddy samples, in order to determine the husked rice yield. The datasets were then obtained from the electrical parameters, moisture content, and husked rice yield. The electrical parameters at 1 MHz were used to train the regression models, in order to predict the moisture content and husked rice yield of paddy. All electrical parameters at 1-8 MHz were used to train four machine learning models: neural networks, decision trees, support vector machines, and random forests. The models were evaluated for correlation and accuracy using determination coefficients (R²) and root mean square errors (RMSE). Results indicated that the dielectric constant and capacitance initially decreased and then increased with increasing frequency. While the dielectric loss decreased consistently. The dielectric loss tangent initially decreased, then increased, and finally decreased again after 6 MHz. Conductivity and electrical conductivity increased consistently. All six electrical parameters increased with the rising moisture content. The regression model was also established using electrical parameters and moisture content. Therefore, the moisture content also increased under the condition of 1 MHz, as the six electrical parameters increased. The determination coefficients of moisture content with the six electrical parameters were ranked in descending order of conductivity, capacitance, dielectric constant, electrical conductivity, dielectric loss tangent, and dielectric loss. Among them, the fitting model with the highest determination coefficient for the conductivity and moisture content was 0.960, with an RMSE of 0.89%. In the fitting model of moisture content and husked rice yield, the determination coefficient was 0.944, with an RMSE of 3.05%. The husked rice yield of paddy increased first and then decreased with rising moisture content. Moreover, the husked rice yield reached the maximum of 69.31% at a moisture content of 13.34%. According to the fitting model of electrical parameters and husked rice yield, the determination coefficients of the six electrical parameters with the husked rice yield were ranked in the descending order of the conductivity, dielectric constant, capacitance, electrical conductivity, dielectric loss tangent, and dielectric loss. Notably, the best fitting was achieved in the conductivity and husked rice yield, with a determination coefficient of 0.929 and an RMSE of 3.08%. Additionally, the neural network achieved the highest performance in predicting the paddy moisture content and husked rice yield among the machine learning models, with the determination coefficients of 0.987 and 0.935, and RMSEs of 0.87% and 1.87%, respectively, indicating the best regression. As such, the neural network model can effectively establish the relationship between electrical parameters and rice quality, thus accurately predicting the moisture content and husked rice yield of paddy. Machine learning was highlighted for the non-destructive testing of agricultural products, in terms of speed and efficiency. The rapid and accurate assessment was also performed on the key quality indicators, such as moisture content and husked rice yield. Meanwhile, this convenient and efficient approach can greatly enhance the efficiency of paddy management for cost savings in the supply chain. The valuable theoretical insights can also provide for the non-destructive quality assessment of the paddy, particularly in the broader application of advanced analytical techniques in agricultural science and industry. [ABSTRACT FROM AUTHOR]

Malaria is a deadly disease that can take a person's life if not predicted or cured correctly. Numerous factors like temperature, humidity, precipitation, etc., impact India's increasing cases of malaria diseases. This research presents an advanced machine learning regression technique recently developed to anticipate the prevalence of malaria in Tripura using a real-world data. The proposed structure uses nine different regression methods, such as multilayer perceptron (MLP), random forest, support vector regressor, gradient boosting regressor, Bayesian ridge, kernel ridge, extreme gradient boost regressor (XGB), light gradient boosting machine (LGBM regressor and linear regression, to predict malaria using the most affecting factors of malaria diseases, namely temperature, humidity, precipitation, month, and years as input. Furthermore, to opt out the best suited technology for malaria cases prediction, the range of value method (ROVM)–multi-criteria decision methods (MCDM) technique has been applied, considering various statistical measurements as criteria. Ultimately, a comparison of various MCDM techniques reveals that MLP, XGB, and RF emerge as the top three choices. MLP regression, with root-mean-square error (RMSE) value of 0.03357, yields the lowest RMSE value, and the coefficient of determination (R2) is 0.97616, yielding the maximum among other regressions. To effectively battle the illness in Tripura, it could be useful for continuing intervention tactics by governmental, profit and nonprofit organizations. [ABSTRACT FROM AUTHOR]

The Brazilian electrical system (BES) relies heavily on hydrothermal energy, specifically hydroelectric power plants (HPPs), which are highly dependent on rainfall patterns. The São Francisco River Basin (SFRB) is a critical component of the BES, playing a key role in electricity generation. However, climate extremes have increasingly impacted energy production in recent decades, posing challenges for HPP management. This study, explores the relationship between extreme precipitation events in the SFRB and two crucial energy variables: Stored Energy (STE) and Affluent Natural Energy (ANE). We analyze the spatial distribution and trends of 11 extreme precipitation indices and investigate the seasonality, trends, and correlations between these energy variables and the extreme indices. Our findings reveal downward trends in both ANE and STE. Additionally, we identify a seasonal pattern influenced by extreme precipitation rates at various time scales. The results indicate that it is possible to estimate ANE and STE efficiently by employing three machine learning (ML) algorithms (Random Forest, Artificial Neural Networks and k-Nearest Neighbors) using extreme precipitation data. These results offer valuable insights for the strategic planning and management of the BES, aiding in decision-making and the development of energy security. [ABSTRACT FROM AUTHOR]

Intelligent transportation and advanced mobility techniques focus on helping operators to efficiently manage navigation tasks in smart cities, enhancing cost efficiency, increasing security, and reducing costs. Although this field has seen significant advances in developing large-scale monitoring of smart cities, several challenges persist concerning the practical assignment of delivery personnel to customer orders. To address this issue, we propose an architecture to optimize the task assignment problem for delivery personnel. We propose the use of different cost functions obtained with deterministic and machine learning techniques. In particular, we compared the performance of linear and polynomial regression methods to construct different cost functions represented by matrices with orders and delivery people information. Then, we applied the Hungarian optimization algorithm to solve the assignment problem, which optimally assigns delivery personnel and orders. The results demonstrate that when used to estimate distance information, linear regression can reduce estimation errors by up to 568.52 km (1.51%) for our dataset compared to other methods. In contrast, polynomial regression proves effective in constructing a superior cost function based on time information, reducing estimation errors by up to 17,143.41 min (11.59%) compared to alternative methods. The proposed approach aims to enhance delivery personnel allocation within the delivery sector, thereby optimizing the efficiency of this process.

The development of nanotechnology has led to the creation of materials with unique properties, and in recent years, numerous attempts have been made to include nanoparticles in concrete in an effort to increase its performance and create concrete with improved qualities. Nanomaterials are typically added to lightweight concrete (LWC) with the goal of improving the composite’s mechanical, microstructure, freshness, and durability qualities. Compressive strength is the most crucial mechanical characteristic for all varieties of concrete composites. For this reason, it is essential to create accurate models for estimating the compressive strength (CS) of LWC to save time, energy, and money. In addition, it provides useful information for planning the construction schedule and indicates when the formwork should be removed. To predict the CS of LWC mixtures made with or without nanomaterials, nine different models were proposed in this study: the gradient-boosted trees (GBT), random forest, tree ensemble, XGBoosted (XGB), Keras, simple regression, probabilistic neural networks, multilayer perceptron, and linear relationship model. A total of 2,568 samples were gathered and examined. The most significant factors influencing CS during the modeling process were taken into account as input variables, including the amount of nanomaterials, cement, water-to-binder ratio, density, the content of lightweight aggregates, type of nano, fine and coarse aggregate content, and water. The performance of the suggested models was assessed using a variety of statistical measures, including the coefficient of determination (R 2), scatter index, mean absolute error, and root-mean-squared error (RMSE). The findings showed that, in comparison to other models, the GBT model outperformed the others in predicting the compression strength of LWC mixtures enhanced with nanomaterials. The GBT model produced the best results, with the greatest value of R 2 (0.9) and the lowest value of RMSE (5.286). Furthermore, the sensitivity analysis showed that the most important factor influencing the prediction of the CS of LWC enhanced with nanoparticles is the water content.

The global demand for energy is significantly impacted by the consumption patterns within the building sector. As such, the importance of energy simulation and prediction is growing exponentially. This research leverages Building Information Modelling (BIM) methodologies, creating a synergy between traditional software methods and algorithm-driven approaches for comprehensive energy analysis. The study also proposes a method for monitoring select energy management factors, a step that could potentially pave the way for the integration of digital twins in energy management systems. The research is grounded in a case study of a newly constructed educational building in New South Wales, Australia. The digital physical model of the building was created using Autodesk Revit, a conventional software for BIM methodology. EnergyPlus, facilitated by OpenStudio, was employed for the traditional software-based energy analysis. The energy analysis output was then used to develop preliminary algorithm models using regression strategies in Python. In this regression analysis, the temperature and relative humidity of each energy unit were used as independent variables, with their energy consumption being the dependent variable. The sigmoid algorithm model, known for its accuracy and interpretability, was employed for advanced energy simulation. This was combined with sensor data for real-time energy prediction. A basic digital twin (DT) example was created to simulate the dynamic control of air conditioning and lighting, showcasing the adaptability and effectiveness of the system. The study also explores the potential of machine learning, specifically reinforcement learning, in optimizing energy management in response to environmental changes and usage conditions. Despite the current limitations, the study identifies potential future research directions. These include enhancing model accuracy and developing complex algorithms to boost energy efficiency and reduce costs. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method to predict the fresh weight of lettuce at the shipping stage in a plant factory using the early-stage growth images. It is well-established that the size and shape of plants correlate with their fresh weight. The proposed method captures chlorophyll fluorescence-based growth images daily and extracts geometric features such as projection area, edge length, and skeleton length. We design a regression model to predict the fresh weight using the dimensionality-reduced historical features. However, without considering growth statuses, the dimensionality reduction approach leads to decreased predictive performance for mature and slower-growing plants. In this paper, we generate a plant growth model that simulates the growth process by integrating multiple growth records based on the comparison of growth statuses. The proposed method then reduces the dimensionality by fitting historical features to the plant growth model to obtain future features. Experimental results demonstrate that the proposed method accurately predicts the fresh weight and achieves the coefficient of determination of 0.885, root mean square error of 8.790 g, and mean absolute error of 6.684 g when predicting the fresh weight ten days ahead using growth images from the past ten days.

Background: An abnormal variation in blood electrolytes, such as potassium, contributes to mortality in children admitted to intensive care units. Continuous and real-time monitoring of potassium serum levels can prevent fatal arrhythmias, but this is not currently practical. The study aims to use machine learning to estimate blood potassium levels with accuracy in real time noninvasively. Methods: Hospitalized patients in the Pediatric Department of the Rajaie Cardiology and Medical Research Center and Tehran Heart Center were recruited from December 2021 to June 2022. The electrocardiographic (ECG) features of patients were evaluated. We defined 16 features for each signal and extracted them automatically. The dimension reduction operation was performed with the assistance of the correlation matrix. Linear regression, polynomials, decision trees, random forests, and support vector machine algorithms have been used to find the relationship between characteristics and serum potassium levels. Finally, we used a scatter plot and mean square error (MSE) to display the results. Results: Of 463 patients (mean age: 8 ± 1 year; 56% boys) hospitalized, 428 patients met the inclusion criteria, with 35 patients having a high noise of ECG were excluded. After the dimension reduction step, 11 features were selected from each cardiac signal. The random forest regression algorithm showed the best performance with an MSE of 0.3. Conclusion: The accurate estimation of serum potassium levels based on ECG signals is possible using machine learning algorithms. This can be potentially useful in predicting serum potassium levels in specific clinical scenarios. [ABSTRACT FROM AUTHOR]

The respiration rate (RR) of dairy cows is a crucial welfare indicator for assessing heat stress in cows exposed to high temperatures. Machine learning (ML) models can automatically identify patterns from factors related to cow RR. This study utilised ML methods to establish a predictive model for cow RR using easily accessible variables in real production settings. A comparison of 20 ML algorithms, including linear regression, neural networks, and others, was conducted to evaluate their performance in predicting cow RR and investigate the impact of different inputs and feature engineering techniques on algorithm performance, using a cleaned dataset comprising 2977 records. The main findings indicate that the CATBOOST-based model, specifically the CATBOOST algorithm with environmental parameters as input features under ordinal encoding, exhibited the best performance, with a coefficient of determination (R2) of 0.676, a mean absolute error (MAE) of 7.246, a mean absolute percentage error (MAPE) of 13.8%, and a root mean square error (RMSE) of 9.341. There was no statistical difference in model performance using environmental parameters, heat indices, or heat flows as input features. Feature polynomials, PCA-based dimensionality reduction, and filter-based feature selection may significantly reduce the performance of the ML-based cow RR model. Additionally, according to the SHAP analysis of the optimal model, air temperature, black globe temperature, and airflow speed are identified as the top three factors contributing to the prediction of cow RR. The findings from this study can offer valuable guidance for the design and regulation of dairy farm environmental control systems. • 20 ML algorithms under 24 feature engineering schemes were evaluated. • The CATBOOST-based model outperformed other models. • SHAP analysis was used to explain the optimal model. [ABSTRACT FROM AUTHOR]

As the world's population grows, the agricultural sectors are destined to increase crop production and security. Improving crop yield by using advanced technologies gives remarkable growth to the economy of the country. Agriculture provides over 20% of India's GDP. Using machine learning algorithms, the crop yield can be predicted which is useful to the farmers to plan the cultivation beforehand. In this work, various machine learning (ML) algorithms are applied to predict the yield of 'rice and sorghum (jowar)' and a novel weighted feature approach with a combination of Support Vector Machine (SVM) and Random Forest (RF) is proposed for two Indian seasons. RF is used to select training data at random, and the learning rate approach from deep learning concepts is implemented to add random weights to each parameter; the SVM model is then trained using the weighted training data. The best weights are again applied for the whole data to implement the SVM and RF algorithms. The weighted feature hybrid model is compared with SVM, RF, Decision tree, Naive Bayes, and k-Nearest Neighbor algorithms. RF-based regression method is also implemented and its ability to predict the crop yield has been discussed based on its performance metrics. The results show that the proposed weighted feature hybrid SVM-RF model gives the best accuracy of 90% when compared with the traditional algorithms. Also, the performances of various ML algorithms for crop yield prediction are analysed and cross-validation of the models is performed and compared, which improved the accuracy by 8-10%. [ABSTRACT FROM AUTHOR]

Turbidity is one of the crucial parameters of water quality. Even though many commercial devices, low-cost sensors, and remote sensing data can efficiently quantify turbidity, they are not valid tools for the classification it. In this paper, we design, calibrate, and test a novel optical low-cost sensor for turbidity quantification and classification. The sensor is based on an RGB light source and a light detector. The analyzed samples are characterized by turbidity values from 0.02 to 60 NTUs, and have four different sources. These samples were generated to represent natural turbidity sources and leaves in the marine areas close to agricultural lands. The data are gathered using 64 different combinations of light, generating complex matrix data. Machine learning models are compared to analyze this data, including training, validation, and test datasets. Moreover, different alternatives for data preprocessing and feature selection are assessed. Concerning the quantification of turbidity, the best results were obtained using averaged data and principal components analyses in conjunction with exponential gaussian process regression, achieving an R2 of 0.979. Regarding the classification of the turbidity, an accuracy of 91.23% is obtained with the fine K-Nearest-Neighbor classifier. The cases in which data were misclassified are characterized by turbidity values lower than 5 NTUs. The obtained results represent an improvement over the current solutions in terms of turbidity quantification and a completely novel approach to turbidity classification. [ABSTRACT FROM AUTHOR]

Solid-state batteries are currently developing into one of the most promising battery types for both the electrification of transport and for energy storage applications due to their high energy density and safe operating behaviour. The performance of solid-state batteries is largely determined by the manufacturing process, particularly in the production of electrodes. However, efficiently analysing the effects of key manufacturing features and predicting the mass loading of electrodes in the early stages of battery manufacturing remain a major challenge. In this study, a machine-learning-based approach is proposed to effectively analyse the importance of manufacturing features and accurately predict the mass loading of electrodes. Specifically, the importance of four key features during the manufacturing process of solid-state batteries is first quantified and analysed using a machine-learning-based method to analyse the importance of features. Then, four effective machine-learning-based regression methods, including decision tree, boosted decision tree, support vector regression and Gaussian process regression, are used to predict the mass loading of the electrodes in the mixing and coating stages. The comparative results show that the developed machine-learning-based approach is able to provide a satisfactory prediction of the electrode mass loading of a solid-state battery with 0.995 R2 while successfully quantifying the importance of four key features in the early manufacturing stages. Due to the advantages of its data-driven nature, the developed machine-learning-based approach can efficiently assist engineers in monitoring/predicting the electrode mass loading of solid-state batteries and analysing/quantifying the importance of manufacturing features of interest. This could benefit the production of solid-state batteries for further energy storage applications. [ABSTRACT FROM AUTHOR]

Integrating a model-based and data-driven approach to generate sufficient number of training data that include time-varying operating conditions is needed to develop a prognostics and health management technique using machine learning for a reusable rocket engine. A data-driven approach was used to predict the engine behavior, which cannot be determined using a model-based approach. This paper proposes a clustered regression model to predict various operating conditions for a reusable rocket engine. The training data were divided in advance according to the operating conditions using the Gaussian mixture model, and multiple regression models were individually trained on each cluster. By switching the regression models according to the operating conditions, the time-series data comprising various operating conditions, such as the combustion and chill-down phases and transition between phases, were accurately predicted when compared with the prediction results of a single regression model. The prediction results of the present hybrid approach agree reasonably well with the static firing test results of a reusable rocket engine if the explanatory variables are adequately included. By integrating model-based and data-driven approaches, adequate training data can be generated for prognostics and health management of the reusable liquid rocket engine. • This study improves the prediction accuracy of the data-driven approach. • Training data for the data-driven approach comprised varying operating conditions. • The varying operating conditions were classified using a Gaussian mixture model. • The regression models were trained based on each classified operating condition. • The prediction accuracy was improved using multiple regression models. [ABSTRACT FROM AUTHOR]

The interdisciplinary research studies of artificial intelligence in health sector is bringing drastic life saving changes in the healthcare domain. One such aspect is the early disease prediction using machine learning and regression algorithms. The purpose of this research is to improve the prediction accuracy of Alzheimer 's disease by analysing the correlation of unexplored Alzheimer causing diseases. The work proposes Chi square-lasso ridge linear (Chi-LRL) model, a new two-tier feature ranking model which recognizes the significance of including diabetes, blood pressure and body mass index as potential Alzhiemer predictive parameters. The newly added predictive parameters of Alzheimer's disease were statistically verified along with the conventional prediction parameters using chi-square method (Chi) as Tier 1 and an embedded model of lasso, ridge and linear (LRL) Regression for feature ranking as Tier 2. The performance of the proposed Chi-LRL model with selected features were then analysed using machine learning algorithms for performance analysis. The result shows a noticeable performance by selecting eleven significant features and a 4.5% increase in the prediction accuracy of Alzheirmer disease. [ABSTRACT FROM AUTHOR]

Abstract Diseases are increasing with exponential rate worldwide. Its detection is challenging task due to unavailability of the experts. Machine learning models provide automated mechanism to detect diseases once trained. It has been used to predict and detect many diseases such as cancer, heart attack, liver infections, kidney infections. The new coronavirus has become one of the deadliest diseases. Its case escalated in unexpected ways. In the literature, many machine learning models such as Extreme Gradient Boosting (XGBoosting), Support Vector Machine (SVM), regression, and Logistic regression have been used. It has been observed that these models can predict COVID cases early but are unable to find the peak point and deadline of the disease. Hence, mathematical models have been designed to early predict and find peak point and dead-line in disease prediction. These mathematical models use integral calculus-based Ordinary Differential Equations (ODEs) to predict COVID cases. Governments are dependent on these models’ pre- diction for early preparation of hospitalization, medicines, and many more. Hence, higher prediction accuracy is required. It has been found in the literature that fractional calculus-based models are more accurate in disease prediction and detection. Fractional models provides to choose order of derivative with fractional value due to which information processing capability increases. In the present work, mathematical model using fractional calculus has been devised for prediction of COVID cases. In the model, quarantine, symptomatic and asymptomatic cases have been incorporated for accurate prediction. It is found that the proposed fractional model not only predicts COVID cases more accurately but also gives peak point and dead-line of the disease.

Corona Virus Disease 2019 (COVID-19) has a significant impact on sleep quality. However, the effects on sleep quality in the post-COVID-19 pandemic era remain unclear, and there is a lack of a screening tool for Chinese older adults. This study aimed to understand the prevalence of poor sleep quality and determine sensitive variables to develop an effective prediction model for screening sleep problems during infectious diseases outbreaks. The Peking University Health Cohort included 10,156 participants enrolled from April to May 2023. The Pittsburgh Sleep Quality Index (PSQI) scale was used to assess sleep quality. The data were randomly divided into a training-testing cohort (n = 7109, 70%) and an independent validation cohort (n = 3027, 30%). Five prediction models with 10-fold cross validation including the Least Absolute Shrinkage and Selection Operator (LASSO), Stochastic Volatility Model (SVM), Random Forest (RF), Artificial Neural Network (ANN), and XGBoost model based on the area under curve (AUC) were used to develop and validate predictors. The prevalence of poor sleep quality (PSQI >7) was 30.69% (3117/10,156). Among the generated models, the LASSO model outperformed SVM (AUC 0.579), RF (AUC 0.626), ANN (AUC 0.615) and XGBoost (AUC 0.606), with an AUC of 0.7. Finally, a total of 12 variables related to sleep quality were used as parameters in the prediction models. These variables included age, gender, ethnicity, educational level, residence, marital status, history of chronic diseases, SARS-CoV-2 infection, COVID-19 vaccination, social support, depressive symptoms, and cognitive impairment among older adults during the post-COVID-19 pandemic. The nomogram illustrated that depressive symptoms contributed the most to the prediction of poor sleep quality, followed by age and residence. This nomogram, based on twelve-variable, could potentially serve as a practical and reliable tool for early identification of poor sleep quality among older adults during the post-pandemic period. The poor sleep quality (PSQI >7) was still prevalent among older adults during the post-COVID-19 pandemic. The LASSO model was the best model to predict poor sleep quality among older adults, compared with SVM, RF, ANN and XGBoost. This prediction model, based on twelve variables, may potentially serve as a practical and reliable tool for the early identification of poor sleep quality among older adults during the post-pandemic period. [ABSTRACT FROM AUTHOR]

Diseases are increasing with exponential rate worldwide. Its detection is challenging task due to unavailability of the experts. Machine learning models provide automated mechanism to detect diseases once trained. It has been used to predict and detect many diseases such as cancer, heart attack, liver infections, kidney infections. The new coronavirus has become one of the deadliest diseases. Its case escalated in unexpected ways. In the literature, many machine learning models such as Extreme Gradient Boosting (XGBoosting), Support Vector Machine (SVM), regression, and Logistic regression have been used. It has been observed that these models can predict COVID cases early but are unable to find the peak point and deadline of the disease. Hence, mathematical models have been designed to early predict and find peak point and dead-line in disease prediction. These mathematical models use integral calculus-based Ordinary Differential Equations (ODEs) to predict COVID cases. Governments are dependent on these models' pre- diction for early preparation of hospitalization, medicines, and many more. Hence, higher prediction accuracy is required. It has been found in the literature that fractional calculus-based models are more accurate in disease prediction and detection. Fractional models provides to choose order of derivative with fractional value due to which information processing capability increases. In the present work, mathematical model using fractional calculus has been devised for prediction of COVID cases. In the model, quarantine, symptomatic and asymptomatic cases have been incorporated for accurate prediction. It is found that the proposed fractional model not only predicts COVID cases more accurately but also gives peak point and dead-line of the disease. [ABSTRACT FROM AUTHOR]

This article introduces a model for accurately predicting students' final grades in the CS1 course by utilizing their grades from the first half of the course. The methodology includes three phases: training, testing, and validation, employing four regression algorithms: AdaBoost, Random Forest, Support Vector Regression (SVR), and XGBoost. Notably, the SVR algorithm outperformed the others, achieving an impressive R-squared (R2) value ranging from 72% to 91%. The discussion section focuses on four crucial aspects: the selection of data features and the percentage of course grades used for training, the comparison between predicted and actual values to demonstrate reliability, and the model's performance compared to existing literature models, highlighting its effectiveness. [ABSTRACT FROM AUTHOR]

Background: Standardized patient‐specific pretreatment dosimetry planning is mandatory in the modern era of nuclear molecular radiotherapy, which may eventually lead to improvements in the final therapeutic outcome. Only a comprehensive definition of a dosage therapeutic window encompassing the range of absorbed doses, that is, helpful without being detrimental can lead to therapy individualization and improved outcomes. As a result, setting absorbed dose safety limits for organs at risk (OARs) requires knowledge of the absorbed dose–effect relationship. Data sets of consistent and reliable inter‐center dosimetry findings are required to characterize this relationship. Purpose: We developed and standardized a new pretreatment planning model consisting of a predictive dosimetry procedure for OARs in patients with neuroendocrine tumors (NETs) treated with 177Lu‐DOTATATE (Lutathera). In the retrospective study described herein, we used machine learning (ML) regression algorithms to predict absorbed doses in OARs by exploiting a combination of radiomic and dosiomic features extracted from patients' imaging data. Methods: Pretreatment and posttreatment data for 20 patients with NETs treated with 177Lu‐DOTATATE were collected from two clinical centers. A total of 3412 radiomic and dosiomic features were extracted from the patients' computed tomography (CT) scans and dose maps, respectively. All dose maps were generated using Monte Carlo simulations. An ML regression model was designed based on ML algorithms for predicting the absorbed dose in every OAR (liver, left kidney, right kidney, and spleen) before and after the therapy and between each therapy session, thus predicting any possible radiotoxic effects. Results: We evaluated nine ML regression algorithms. Our predictive model achieved a mean absolute dose error (MAE, in Gy) of 0.61 for the liver, 1.58 for the spleen, 1.30 for the left kidney, and 1.35 for the right kidney between pretherapy 68Ga‐DOTATOC positron emission tomography (PET)/CT and posttherapy 177Lu‐DOTATATE single photon emission (SPECT)/CT scans. Τhe best predictive performance observed was based on the gradient boost for the liver, the left kidney and the right kidney, and on the extra tree regressor for the spleen. Evaluation of the model's performance according to its ability to predict the absorbed dose in each OAR in every possible combination of pretherapy 68Ga‐DOTATOC PET/CT and any posttherapy 177Lu‐DOTATATE treatment cycle SPECT/CT scans as well as any 177Lu‐DOTATATE SPECT/CT treatment cycle and the consequent 177Lu‐DOTATATE SPECT/CT treatment cycle revealed mean absorbed dose differences ranges from −0.55 to 0.68 Gy. Incorporating radiodosiomics features from the 68Ga‐DOTATOC PET/CT and first 177Lu‐DOTATATE SPECT/CT treatment cycle scans further improved the precision and minimized the standard deviation of the predictions in nine out of 12 instances. An average improvement of 57.34% was observed (range: 17.53%–96.12%). However, it's important to note that in three instances (i.e., Ga,C.1 → C3 in spleen and left kidney, and Ga,C.1 → C2 in right kidney) we did not observe an improvement (absolute differences of 0.17, 0.08, and 0.05 Gy, respectively). Wavelet‐based features proved to have high correlated predictive value, whereas non‐linear‐based ML regression algorithms proved to be more capable than the linear‐based of producing precise prediction in our case. Conclusions: The combination of radiomics and dosiomics has potential utility for personalized molecular radiotherapy (PMR) response evaluation and OAR dose prediction. These radiodosiomic features can potentially provide information on any possible disease recurrence and may be highly useful in clinical decision‐making, especially regarding dose escalation issues. [ABSTRACT FROM AUTHOR]

To understand the temporal and spatial variability of thermal refuges, this study focused on modeling potential thermal refuge area (PTRA) at a sub‐daily time‐step in two tributary confluences of the Sainte‐Marguerite River (Canada) during the summers of 2020 and 2021. Aquatic ectotherm species, such as Atlantic salmon (Salmo salar), seek these refuges to avoid heat stress during high summer river temperatures. To investigate the temporal variability of these PTRA, we employed inverse weighted distance interpolation to delineate the hourly area available at both confluences. We then analyzed the impact of the atypical low flow conditions of summer 2021 on the diel cycle of PTRA extremes using the coefficient of variation and the generalized additive model (GAM). Finally, we used four supervised machine‐learning regression models and three to five hydrometeorological predictors to estimate hourly PTRA availability: multivariate adaptive splines regression (MARS), GAM, support vector machine regression (SVM), and random forest regression (RF). The results showed that tree‐based and kernel‐based regression models, RF and SVM, outperformed GAM and MARS. RF had the highest accuracy at both sites, with a relative root mean square error and Nash–Sutcliffe efficiency coefficient (Nash) of 13% and 93%, respectively. Our study discovered that under warm conditions in August 2021, small perennial tributary inflows in combination with low mainstem discharge could create high and constant PTRA at confluences, potentially providing vital thermal refuges for cold‐water taxa. These refuges may be especially important at the local level, within a specific stretch or section of the river. Given the decreasing availability of thermal refuges for salmonids, it is crucial to monitor stream temperatures at small spatial and temporal scales using data‐driven techniques in order to understand stream temperature heterogeneity at tributary confluences. [ABSTRACT FROM AUTHOR]

Currently, historical data and on-site surveys—particularly in the context of China—are heavily relied upon to determine the best time to maintain expressway road markings. This study aims to determine what influences the service life of thermoplastic road markings on expressways in Shandong Province, China, while considering both those motorways' unique characteristics and the local environment. Additionally, a scientific evaluation of the road markings' retroreflective coefficient's decay pattern will be undertaken. We collected the retroreflective data for twelve consecutive months regarding the thermoplastic road markings on five expressways and potential influencing factors such as age of marking and annual average daily traffic. The service life of the markings was forecast using a multiple linear regression. Dominance analysis was used to quantitatively analyze each explanatory factor's impact on the service life of the markings, and statistically significant variables were also found. Using LightGBM, a machine learning technique, a nonparametric prediction model was also created based on examining the relevance of influencing elements. The modeling results show that LightGBM generates an R2 of 0.942, implying that it offers better interpretability and higher accuracy than the regression-based approach. Additionally, LightGBM outperforms MLR according to final validation accuracies, with a score of 95.02% or more than 8% that of MLR. The results are useful for expressway marking upkeep and for driving safety. [ABSTRACT FROM AUTHOR]

The uranium required for power plants is mainly extracted by two methods in roughly equal amounts: quarries (underground and open pit) and in situ leaching (ISL). Uranium mining by in situ leaching is extremely attractive because it is economical and has a minimal impact on the region's ecology. The effective use of ISL requires, among other things, the accurate assessment of the host rocks' filtration characteristics. An accurate assessment of the filtration properties of the host rocks allows optimizing the mining process and improving the quality of the ore reserve prediction. At the same time, in Kazakhstan, this calculation is still based on methods that were developed more than 50 years ago and, in some cases, produce inaccurate results. According to our estimates, this method provides a prediction of filtration properties with a determination coefficient R 2 = 0.32. This paper describes a method of calculating the filtration coefficient of ore-bearing rocks using machine learning methods. The proposed approach was based on nonlinear regression models providing a 20–75% increase in the accuracy of the filtration coefficient assessment compared with the current methodology. The work used different types of machine learning algorithms based on the gradient boosting technique, bagging technique, feed-forward neural networks, support vector machines, etc. The results of logging, core sampling, and hydrogeological studies obtained during the exploration stage of the Inkai deposit were used as the initial data. All used machine learning models demonstrated significantly better results than the old method. This resulted in improved results compared with previous studies. The LightGBM regressor demonstrated the best result ( R 2 = 0.710). [ABSTRACT FROM AUTHOR]

The global demand for energy is significantly impacted by the consumption patterns within the building sector. As such, the importance of energy simulation and prediction is growing exponentially. This research leverages Building Information Modelling (BIM) methodologies, creating a synergy between traditional software methods and algorithm-driven approaches for comprehensive energy analysis. The study also proposes a method for monitoring select energy management factors, a step that could potentially pave the way for the integration of digital twins in energy management systems. The research is grounded in a case study of a newly constructed educational building in New South Wales, Australia. The digital physical model of the building was created using Autodesk Revit, a conventional software for BIM methodology. EnergyPlus, facilitated by OpenStudio, was employed for the traditional software-based energy analysis. The energy analysis output was then used to develop preliminary algorithm models using regression strategies in Python. In this regression analysis, the temperature and relative humidity of each energy unit were used as independent variables, with their energy consumption being the dependent variable. The sigmoid algorithm model, known for its accuracy and interpretability, was employed for advanced energy simulation. This was combined with sensor data for real-time energy prediction. A basic digital twin (DT) example was created to simulate the dynamic control of air conditioning and lighting, showcasing the adaptability and effectiveness of the system. The study also explores the potential of machine learning, specifically reinforcement learning, in optimizing energy management in response to environmental changes and usage conditions. Despite the current limitations, the study identifies potential future research directions. These include enhancing model accuracy and developing complex algorithms to boost energy efficiency and reduce costs.

This article introduces a model for accurately predicting students’ final grades in the CS1 course by utilizing their grades from the first half of the course. The methodology includes three phases: training, testing, and validation, employing four regression algorithms: AdaBoost, Random Forest, Support Vector Regression (SVR), and XGBoost. Notably, the SVR algorithm outperformed the others, achieving an impressive R-squared (R2) value ranging from 72% to 91%. The discussion section focuses on four crucial aspects: the selection of data features and the percentage of course grades used for training, the comparison between predicted and actual values to demonstrate reliability, and the model’s performance compared to existing literature models, highlighting its effectiveness.

AbstractBackground Corona Virus Disease 2019 (COVID-19) has a significant impact on sleep quality. However, the effects on sleep quality in the post-COVID-19 pandemic era remain unclear, and there is a lack of a screening tool for Chinese older adults. This study aimed to understand the prevalence of poor sleep quality and determine sensitive variables to develop an effective prediction model for screening sleep problems during infectious diseases outbreaks.Materials and Methods The Peking University Health Cohort included 10,156 participants enrolled from April to May 2023. The Pittsburgh Sleep Quality Index (PSQI) scale was used to assess sleep quality. The data were randomly divided into a training-testing cohort (n = 7109, 70%) and an independent validation cohort (n = 3027, 30%). Five prediction models with 10-fold cross validation including the Least Absolute Shrinkage and Selection Operator (LASSO), Stochastic Volatility Model (SVM), Random Forest (RF), Artificial Neural Network (ANN), and XGBoost model based on the area under curve (AUC) were used to develop and validate predictors.Results The prevalence of poor sleep quality (PSQI >7) was 30.69% (3117/10,156). Among the generated models, the LASSO model outperformed SVM (AUC 0.579), RF (AUC 0.626), ANN (AUC 0.615) and XGBoost (AUC 0.606), with an AUC of 0.7. Finally, a total of 12 variables related to sleep quality were used as parameters in the prediction models. These variables included age, gender, ethnicity, educational level, residence, marital status, history of chronic diseases, SARS-CoV-2 infection, COVID-19 vaccination, social support, depressive symptoms, and cognitive impairment among older adults during the post-COVID-19 pandemic. The nomogram illustrated that depressive symptoms contributed the most to the prediction of poor sleep quality, followed by age and residence.Conclusions This nomogram, based on twelve-variable, could potentially serve as a practical and reliable tool for early identification of poor sleep quality among older adults during the post-pandemic period.

The accurate prediction of strip crown is the precondition of the shape preset model in hot strip rolling. In this study, a new hybrid strip crown forecasting model is proposed in combination with extreme learning machine (ELM) and the industrial data. The production data of 1780 mm hot strip rolling are collected by the on-site data acquisition system to form dataset. Principal component analysis (PCA) is used to reduce the dimension of the input data for modeling samples. To improve the prediction accuracy of ELM, an improved PSO based on S-curve decreasing inertia weight (SDWPSO) is proposed to optimize the initial weights and biases of ELM. Finally, the optimal ELM model and simple production dataset are used to establish a strip crown prediction model of hot strip rolling named PCA-SDWPSO-ELM. The comprehensive performance of the proposed hybrid PCA-SDWPSO-ELM prediction model is evaluated by MAE, MAPE and RMSE. The superiority of the proposed model is also proved by comparing the prediction results with those of the other three comparison models. The research shows that the hybrid PCA-SDWPSO-ELM method can solve the problem of nonlinear and strong coupling in traditional engineering. It is suitable for parameter prediction and optimization in the iron and steel manufacturing industry, especially in the process of shape control in hot strip rolling. [ABSTRACT FROM AUTHOR]

Magnesium matrix composites have attracted significant attention due to their lightweight nature and impressive mechanical properties. However, the fabrication process for these alloy composites is often time-consuming, expensive, and labor-intensive. To overcome these challenges, this study introduces a novel use of machine learning (ML) techniques to predict the mechanical properties of magnesium matrix composites, providing an innovative and cost-effective alternative to conventional methods. Various regression models, including decision tree regression, random forest regression, extra tree regression, and XGBoost regression, were employed to forecast the yield strength of magnesium alloy composites reinforced with diverse materials. This approach leverages existing research data on matrix type, reinforcement type, heat treatment, and mechanical working. The XGBoost Regression model outperformed the others, exhibiting an R2 value of 0.94 and the lowest error rate. Feature importance analysis from the best model indicated that the reinforcement particle form had the most significant influence on the mechanical properties. Our research also identified the optimized parameters for achieving the highest yield strength at 186.99 MPa. This study successfully demonstrated the effectiveness of ML as a valuable, novel tool for optimizing the production parameters of magnesium matrix composites. [ABSTRACT FROM AUTHOR]

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The condition-based maintenance system(CBMS) is a technology to improve the survivability and operational performance of naval ships. It monitors the condition of major equipment (propulsion system, power system, etc.) and performs functions such as displaying trends through performance evaluation, generating alarm conditions, fault diagnosis, and predicting remaining life. This paper studies ways to increase the accuracy and efficiency of machine learning model design for abnormal diagnosis of Naval propulsion system equipment. The Random Forest and Support Vector Regression(SVR) models used in overseas cases and the XGBoost and Light GBM models, which are most commonly used in various fields, were used to calculate the normal prediction values(Baseline) based on actual operational naval ship propulsion system equipment, and performed condition diagnosis through comparison with real-time measurements. Then, the performance of each regression model was compared and analyzed through RMSE, R2 Score, and learning time. [ABSTRACT FROM AUTHOR]

Rutting is a typical distress of asphalt pavement related to material, structural, loading, and environmental conditions of the pavement. This work presents a thorough and targeted synthesis of literature on current predictive models for rutting development in asphalt pavement, including the mechanical model, empirical model, machine learning model, and their combinations. By introducing and comparing the characteristics, advantages, and limitations of different model types, we focused on suitable approaches that predict rutting given the available information in the corresponding studies. Furthermore, we conducted a practitioner survey to identify performance deterioration models used by various highway agencies for asphalt pavement and to capture insights and experiences of users on the existing models in terms of reliability, precision, input and output parameters, consideration of maintenance and rehabilitation history, implementation considerations, etc. This review sheds light on the developing trend of predictive models for rutting and other distresses of asphalt pavement. [ABSTRACT FROM AUTHOR]

Over the last few years, researchers have shown a growing interest in polyvinyl chloride (PVC) gasification and have conducted several studies to evaluate and enhance the process. These studies have recognized that processing parameters have a crucial impact on the assessment of PVC gasification. Despite this, there has been limited exploration of the use of machine learning techniques, particularly regression models, to optimize PVC waste gasification. This study aims to investigate the effectiveness of regression models as machine learning algorithms in predicting the performance of PVC waste gasification. The study uses data collected through a validated thermodynamic model, and three different regression models are tested and compared in detail. Cold gas efficiency and normalized carbon dioxide emission are predicted using linear, quadratic, and quadratic with interaction algorithms. The outcomes for emission algorithms reveal that the linear emission algorithm possesses a high R-square value of 97.49%, which indicates its strong predictive capability. Nevertheless, the quadratic algorithm outperforms it, exhibiting an R-square value of 99.81%. The quadratic algorithm with an interaction term, however, proves to be the best among them all, displaying a perfect R-square value of 99.90%. A similar observation is detected for the cold gas efficiency algorithms. These findings suggest that the quadratic algorithm with an interaction term is superior and has a greater predictive accuracy. This research is expected to provide valuable insight into how regression algorithms can be used to maximize the efficiency of PVC waste gasification and reduce its associated environmental concerns. [ABSTRACT FROM AUTHOR]