Random Regression Research Articles

Optimization of process parameters of selective laser melted nickel-based superalloy for densification by random forest regression algorithm and response surface methodology

In this study, Random Forest Regression (RFR) and Response Surface Methodology (RSM) were employed to predict the optimized processing parameters to achieve the highest densification of a nickel-based superalloy Mar-M247LC fabricated by selective laser melting (SLM). The RFR model considered input processing parameters such as laser power, hatch distance, and scanning speed. A dataset of 223 samples, was used to train the RFR model. As a result, the RFR model exhibited accuracy of 99.57 %, R2 value of 0.976, Mean Square Error (MSE) of 0.402, and Mean Absolute Percentage Error (MAPE) of 0.426 % on testing set. In addition to the RFR model, this study also employed Central Composite Design (CCD) and RSM to optimize the processing parameter sets. Subsequently, this study conducted Box-Behnken Design (BBD) to experimentally validate the accuracy of this RFR model. In the end, a set of the optimal processing parameters was tested and resulted a sample densification of 99.959 %, outperformed that in the original database before building the RFR model, which was 99.734 %. In summary, the RFR models was able to predict densification with accuracy, and by coupling with RSM, the optimal processing parameter could be obtained, so better densification of the build could be achieved.

Application of machine learning in predicting the thermal conductivity of single-filler polymer composites

Polymer composites with superior thermal conductivity and low electrical conductivity are pivotal in the cooling of electronic devices. Despite their prevalence, the accurate prediction of the thermal conductivity of these composites remains a challenge. The emergence of machine learning (ML) provides a groundbreaking approach to solving this issue. In this study, we constructed a comprehensive dataset collected from previous experimental papers and successfully predicted the thermal conductivity of single-filler polymer composites using four ML regression algorithms: random forest regression (RFR), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and Gaussian process regression (GPR). By employing feature engineering to select pertinent features from the original datasets, the accuracy of the four models on the test set was improved, among which GBDT exhibited the highest accuracy with the Pearson correlation coefficient value of 0.981. Factors such as filler volume fraction and matrix thermal conductivity significantly influence the thermal conductivity of composite materials, while the thermal conductivity of the fillers has a relatively minor impact. Additionally, we identified the topological polar surface area (TPSA) as a crucial descriptor for surface modifications, quantifying diverse surface-modifying agents. Due to the incorporation of more descriptors, ML models exhibit higher precision and broader applicability compared to empirical formulas. Our study provides an effective tool for predicting the thermal conductivity of polymer composites with single fillers and underscores the potential of machine learning in accelerating materials design.

Water for Food in the Tigris–Euphrates River System

Water scarcity is an important threat to food security in the Tigris–Euphrates river system. Water scarcity is a huge worldwide problem that results from the rapid increase in water demand, which exceeds the amount of available water. The most significant problems currently affecting countries are food insecurity water scarcity. The Tigris–Euphrates river system countries suffer from different political issues, such as the Syrian war and internal civil conflicts in Iraq. In addition, this area consists of only three countries: Iraq, Syria, and Turkey, but it affects the entire Middle East. Turkey has established many irrigation projects compared to Iraq, which still suffers from the previous American invasion. Therefore, this study examines the Tigris–Euphrates river system (using two countries) to examine the relationship between water scarcity and food security from 1992 to 2020. This study will be conducted using a fixed and random regression approach over 18 years. The results show a negative relationship between water scarcity and food security in the short run, at a 10% significance level, and a long-term positive relationship of 1%. Thus, the use of research and development and the encouragement of investments will help policymakers to develop a nexus between water scarcity and food security.

Enhancing Bitcoin Price Volatility Estimator Predictions: A Four-Step Methodological Approach Utilizing Elastic Net Regression

This paper provides a computationally efficient and novel four-step methodological approach for predicting volatility estimators derived from bitcoin prices. In the first step, open, high, low, and close bitcoin prices are transformed into volatility estimators using Brownian motion assumptions and logarithmic transformations. The second step determines the optimal number of time-series lags required for converting the series into an autoregressive model. This selection process utilizes random forest regression, evaluating the importance of each lag using the Mean Decrease in Impurity (MDI) criterion and optimizing the number of lags considering an 85% cumulative importance threshold. The third step of the developed methodological approach fits the Elastic Net Regression (ENR) to the volatility estimator’s dataset, while the final fourth step assesses the predictive accuracy of ENR, compared to decision tree (DTR), random forest (RFR), and support vector regression (SVR). The results reveal that the ENR prevails in its predictive accuracy for open and close prices, as these prices may be linear and less susceptible to sudden, non-linear shifts typically seen during trading hours. On the other hand, SVR prevails for high and low prices as these prices often experience spikes and drops driven by transient news and intra-day market sentiments, forming complex patterns that do not align well with linear modelling.

Two-Stage Machine Learning-Based Approach to Predict Points of Departure for Human Noncancer and Developmental/Reproductive Effects.

Chemical points of departure (PODs) for critical health effects are crucial for evaluating and managing human health risks and impacts from exposure. However, PODs are unavailable for most chemicals in commerce due to a lack of in vivo toxicity data. We therefore developed a two-stage machine learning (ML) framework to predict human-equivalent PODs for oral exposure to organic chemicals based on chemical structure. Utilizing ML-based predictions for structural/physical/chemical/toxicological properties from OPERA 2.9 as features (Stage 1), ML models using random forest regression were trained with human-equivalent PODs derived from in vivo data sets for general noncancer effects (n = 1,791) and reproductive/developmental effects (n = 2,228), with robust cross-validation for feature selection and estimating generalization errors (Stage 2). These two-stage models accurately predicted PODs for both effect categories with cross-validation-based root-mean-squared errors less than an order of magnitude. We then applied one or both models to 34,046 chemicals expected to be in the environment, revealing several thousand chemicals of moderate concern and several hundred chemicals of high concern for health effects at estimated median population exposure levels. Further application can expand by orders of magnitude the coverage of organic chemicals that can be evaluated for their human health risks and impacts.

Root Respiration–Trait Relationships Are Influenced by Leaf Habit in Tropical Plants

Root respiration is a critical physiological trait that significantly influences root system activity. Recent studies have associated root respiration with the economic functioning of roots; however, research on root respiration in tropical plants remains limited. This study examined the fine root respiration and the relationship between root respiration and root chemical and morphological traits in 16 tropical plant species, including both evergreen and deciduous species. Findings revealed that deciduous species exhibited higher root respiration compared to evergreen species. Root respiration positively correlated with root nitrogen concentration and specific root length and correlated negatively with root diameter and root tissue density across all species. The root respiration patterns in evergreen species aligned with those seen in all tree species, while deciduous species showed a distinct negative correlation with root tissue density and no significant correlations with other root traits. Principal component analysis revealed that the patterns of root variation in both evergreen and deciduous trees were multidimensional, with deciduous trees exhibiting acquisitive traits and evergreen trees displaying conservative traits. Random forest and multiple regression analysis showed that specific root length exerted the most significant influence on root respiration in both evergreen and deciduous trees. These findings are ecologically significant, enhancing our understanding of root respiration in tropical plants and its impact on ecosystem functions. They contribute valuable insights and support the conservation and management of tropical vegetation.

Approximation of reliabilities for random-regression single-step genomic best linear unbiased predictor models

Random-regression models (RRM) are used in national genetic evaluations for longitudinal traits. The outputs of RRM are an index based on random regression coefficients and its reliability. The reliabilities are obtained from the inverse of the coefficient matrix of mixed model equations (MME). The reliabilities must be approximated for large data sets because it is impossible to invert the MME. There is no extensive literature on methods to approximate the reliabilities of RRM when genomic information is included by single-step GBLUP. We developed an algorithm to approximate such reliabilities. Our method combines the reliability of the index without genomic information with the reliability of a GBLUP model in terms of effective record contributions. We tested our algorithm in the 3-lactation model for milk yield from the Czech Republic. The data had 30 million test-day records, 2.5 million animals in the pedigree, and 54,000 genotyped animals. The correlation between our approximation and the reliabilities obtained from the inversion of the MME was 0.98, and the slope and intercept of the regression were 0.91 and 0.02, respectively. The elapsed time to approximate the reliabilities for the Czech data was 21 min.

Modeling the impact of driving aggression on lane change performance Measures: Steering compensatory Behavior, lane change execution duration and crash probability

The current study aimed to investigate the influence of driving aggression on lane change behavior. Drivers belonging to three levels of aggressive driving styles (aggressive, moderately aggressive, and non-aggressive) participated in lane change experiments, and 232 lane change maneuvers were recorded. Steering behavior during lane change maneuvers was investigated using three steering performance measures, including the standard deviations of steering angle, steering reversal rates, and maximum steering swerve. Three separate generalized estimating equation models were developed to analyze the effects of aggressive driving behavior on the standard deviation of steering angle, steering reversal rates, and maximum steering swerve. A Weibull accelerated failure time model with clustered heterogeneity was developed to analyze the effect of aggressive driving behavior on lane change execution duration. Additionally, to model the crash probability during lane change, a random effects logistic regression model was developed. The findings indicate that aggressive and moderately aggressive drivers completed lane changes 59% and 38% faster, respectively, compared to non-aggressive drivers. This implies a propensity for aggressive and moderately aggressive drivers to execute lane changes more swiftly than non-aggressive drivers. Aggressive drivers increased their standard deviation of steering angle, steering reversal rates, and maximum steering swerves by 180%, 60%, and 62%, respectively, compared to non-aggressive drivers. This implies that aggressive drivers engaged in extreme steering behavior as a compensatory measure for their reckless driving in order to avoid a collision during lane change maneuvering. However, despite their vigorous compensatory steering behavior, the crash rate (number of crashes observed) increased by 5.10 and 2.83 times for aggressive and moderately aggressive drivers, respectively, compared to non-aggressive drivers. The findings have implications for developing driving assistance systems and lane change simulation models.

Workforce forecasting for state transportation agencies: A machine learning approach

A decline in the number of construction engineers and inspectors at State Transportation Agencies (STAs) to manage the ever-increasing lane miles has emphasized the importance of workforce planning in these agencies. Forecasting workforce requirements is crucial for effective planning in any industry or agency. This study developed machine learning models to estimate the person-hour requirements of STAs at the project level. The Arkansas Department of Transportation (ARDOT) was used as a case study, using its employee and project details data between 2012 and 2021. Machine learning regression models ranging from linear, tree ensembles, kernel-based, and neural network-based models were developed. These models were compared based on the accuracy of their predictions, the time taken for training the models and their prediction time. Predictions were tested based on the K-fold cross validation technique. The results indicated a high performance from the random forest regression model, a tree ensemble with bagging, which recorded a mean R-squared value of 0.91. Other machine learning models such as an ensemble neural networks model and the linear models also proved to be fit for the problem, attaining R squared value as high as 0.80 and 0.78 respectively. These findings underscore the capability of machine learning models to provide more accurate workforce demand forecasts for STAs and the construction industry. This enhanced accuracy in workforce planning will contribute to improved resource allocation and management.

Optical frequency multiplication using residual network with random forest regression

In this work, we present a method for optical frequency multiplication utilizing a hybrid deep learning approach that integrates the Residual Network (ResNet) with the Random Forest Regression (RFR) algorithm. Three different frequency multiplication modulation schemes are adopted to illustrate the method, which can obtain suitable parameters for these schemes. Based on the parameters predicted by the algorithm, the 8-tupling, 12-tupling, and 16-tupling mm-wave signals are generated by numerical simulation. The simulation results show that for 8-tupling frequency multiplication, an OSSR (optical sideband suppression ratio) is 30.73 dB and an RFSSR (radio frequency spurious suppression ratio) of 80 GHz is 42.29 dB. For 12-tupling frequency multiplication, the OSSR is 30.09 dB, and the RFSSR of the 120 GHz mm wave is 36.21 dB. For generating 16-tupling frequency mm-wave, an OSSR of 29.86 dB and an RFSSR of 34.52 dB are obtained. In addition, the impact of amplitude fluctuation and bias voltage drift on the quality of mm-wave signals is also studied.

CORPORATE GOVERNANCE DAN KEPUTUSAN STRUKTUR MODAL PADA PERUSAHAAN MANUFAKTUR DI INDONESIA

This study aims to determine whether corporate governance such as gender diversity, board size and board independence, as well as company characteristics such as profitability affect capital structure decisions. This study uses panel data from 55 manufacturing sector companies listed on the Indonesian stock exchange from 2014 to 2021. This study uses a random effect regression analysis technique to test the hypothesis, the results of this study are the variables gender diversity, board size and board independence and profitability affect leverage significantly, for corporate governance variables, namely gender diversity has a negative effect on leverage, board size has a positive effect, board independence has a positive effect and the company's characteristic variable, namely profitability, has a negative effect on leverage. These findings suggest that companies should be careful in capital structure decisions to avoid the risk of default in addition to offering support to company managers and investors in establishing and expecting an optimal capital structure and to policy makers and regulators to develop institutional support to increase the effectiveness of the corporate governance mechanism

Optimization of a non-pneumatic tire using design of experiments and machine learning techniques

In this paper, the spoke structure of a non-pneumatic tire is optimized. The finite element model of the spoke structure is created and structural dynamic analysis is made using ABAQUS. The optimization goal is to simultaneously reduce three characteristics of the spoke structure, the mass, vertical displacement and acceleration. Simulations are performed according to the center composite design table, and the objective function is estimated using response surface analysis (RSA), random forest regression (RFR), gradient boosting regression (GBR) and artificial neural network (ANN). The genetic algorithm is used for minimization, and the optimization results of each objective function are analyzed through verification simulation. As a result of the optimization, the error between the predicted value and the actual value is 18.6% for RSA 3.1% for RFR, 1.7% for GBR, and 15.0% for the ANN. The ANN proposes the optimum design variables which derive output values smaller than the current minimum value because the ANN properly represents the nonlinear characteristics of the spoke model generated in this study. Highlights Simulation based optimization of the spoke structure of a non-pneumatic tire Various regression models are used and their accuracy is compared A two-step genetic algorithm is used for optimization

Risk-adjusted trend in national inpatient fall rates observed from 2011 to 2019 in acute care hospitals in Switzerland: a repeated multicentre cross-sectional study

ObjectivesThis study aimed to investigate whether a significant trend regarding inpatient falls in Swiss acute care hospitals between 2011 and 2019 could be confirmed on a national level, and whether...

Predicting Soccer Players' Fitness Status Through a Machine-Learning Approach.

The study had 3 purposes: (1)to develop an index using machine-learning techniques to predict the fitness status of soccer players, (2)to explore the index's validity and its relationship with a submaximal run test (SMFT), and (3)to analyze the impact of weekly training load on the index and SMFT outcomes. The study involved 50 players from an Italian professional soccer club. External and internal loads were collected during training sessions. Various machine-learning algorithms were assessed for their ability to predict heart-rate responses during the training drills based on external load data. The fitness index, calculated as the difference between actual and predicted heart rates, was correlated with SMFT outcomes. Random forest regression (mean absolute error = 3.8 [0.05]) outperformed the other machine-learning algorithms (extreme gradient boosting and linear regression). Average speed, minutes from the start of the training session, and the work:rest ratio were identified as the most important features. The fitness index displayed a very large correlation (r = .70) with SMFT outcomes, with the highest result observed during possession games and physical conditioning exercises. The study revealed that heart-rate responses from SMFT and the fitness index could diverge throughout the season, suggesting different aspects of fitness. This study introduces an "invisible monitoring" approach to assess soccer player fitness in the training environment. The developed fitness index, in conjunction with traditional fitness tests, provides a comprehensive understanding of player readiness. This research paves the way for practical applications in soccer, enabling personalized training adjustments and injury prevention.

Data-driven approach for intelligent tunnel dust concentration prediction

The precise evaluation of tunnel dust concentration (TDC) stands as a primary concern within engineering practices. However, comprehensive and accurate prediction of TDC becomes increasingly challenging due to the expanding multi-source datasets and cumulative errors. This study employs innovative data-driven ensemble learning methodologies, specifically random forest (RF) and gradient boosting regression tree (GBRT), to delineate the intricate relationships between TDC values and various rock tunnel characteristics. Five diverse variables sourced from multiple origins are examined and employed as inputs within the database, while the TDC values derived from the dust concentration meter serve as the target outputs. Subsequently, a Bayesian optimization approach, the Tree-structured Parzen Estimator (TPE), is introduced to automatically ascertain the optimal hyper-parameters for the ensemble models. A comprehensive comparison is conducted between the two ensemble learning models and a singular machine learning algorithm, the classification and regression tree (CART), concerning predictive accuracy and resilience via 10-fold cross-validation (CV). The findings reveal the superior performance of the hybrid ensemble learning models over the individual ML models. Notably, the TPE-GBRT algorithm adeptly captures the measurement evolution, showcasing the lowest prediction errors and the highest correlation coefficient. These predicted outcomes significantly contribute to enhancing the engineering comprehension of the interrelation between rock tunnel parameters and TDC values.

Optimizing Communication on HPV Vaccination to Parents of 11- to 14-Year-Old Adolescents in France: A Discrete Choice Experiment.

With the aim to optimize communication during HPV vaccination campaigns in France, we elicited parental preferences around HPV vaccination. We conducted a single-profile discrete choice experiment (DCE) among parents of 11- to 14-year-old middle-school pupils, who completed an anonymous, self-administered, internet-based questionnaire during 2020-2021. The DCE comprised five attributes (vaccine-preventable disease, justification of optimal age, information on safety, indirect protection and coverage) of vaccination against an unnamed disease that were presented to respondents in ten choice tasks, or scenarios. We use fixed effect logit models to estimate attribute weights on theoretical vaccine acceptance, and random effect linear regression to estimate attribute coefficients on vaccine eagerness (decision and decision certainty). We estimated marginal effects of attributes on expected vaccine acceptance. Vaccination scenarios were accepted by 55.6-89.2% of the 1291 participants. The largest marginal effects on expected vaccine acceptance in the full sample arose from prevention of cancer versus genital warts (+11.3 percentage points); from a "severe side effect suspicion that was not scientifically confirmed" versus a statement about "more benefits than risks" (+8.9 percentage points), and information on 80% vaccine coverage in neighbouring countries versus on "insufficient coverage" (+4.2 percentage points). Explaining the early age of vaccination by sexual debut had a strong negative impact among French monolingual parents with lower education level (vs age-independent, OR 0.48, 95% CI 0.27-0.86), but not other socio-economic groups. After removing low-quality responses (unvaried certainty and short questionnaire completion), among serial non-demanders with children not vaccinated against HPV, only disease elimination impacted vaccine eagerness positively (coefficient 0.54, 0.06-1.02). Using DCEs to elicit parents' preferences around communication messages, notably on cancer prevention, vaccine coverage and information about vaccine safety, could help to optimize HPV vaccination promotion efforts.

Biomass Higher Heating Value Estimation: A Comparative Analysis of Machine Learning Models

The research conducted focused on the capabilities of various non-linear and machine learning (ML) models in estimating the higher heating value (HHV) of biomass using proximate analysis data as inputs. The research was carried out to identify the most appropriate model for the estimation of HHV, which was determined by a statistical analysis of the modeling error. In this sense, artificial neural networks (ANNs), support vector machine (SVM), random forest regression (RFR), and higher-degree polynomial models were compared. After statistical analysis of the modeling error, the ANN model was found to be the most suitable for estimating the HHV biomass and showed the highest specific regression coefficient, with an R2 of 0.92. SVM (R2 = 0.81), RFR, and polynomial models (R2 = 0.84), on the other hand, also exhibit a high degree of estimation, albeit with somewhat larger modelling errors. The study conducted suggests that ANN models are best suited for the non-linear modeling of HHV of biomass, as they can generalize and search for links between input and output data that are more robust but also more complex in structure.

INDUSTRIAL GROWTH, HUMAN DEVELOPMENT INDEX, GENDER DEVELOPMENT INDEX AND POPULATION ON THE SEVERITY OF POVERTY IN CENTRAL JAVA

Poverty is a problem that affects most countries in the world, including Indonesia. Central Java Province is a region on the island of Java that occupies the second position with the highest poverty rate compared to other provinces on the island of Java. The problem of poverty must be addressed immediately so that an analysis of the factors that influence poverty is needed so that it is hoped that poverty alleviation programs can be right on target. This research aims to examine the influence of industrial growth, HDI, population and GDI on poverty levels in Central Java Province in 2011-2020. The data used is panel data which is analyzed using Random Effect Model panel data regression with a total of 35 regencies/cities in Central Java as research objects. The processing results show that there is a negative and significant influence between Industrial Growth, HDI and GDI on Poverty Levels. Meanwhile, population size does not have a significant influence on poverty levels. This means that with industrial growth, HDI and GDI development can reduce the severity of poverty. The Central Java Provincial Government must create a good environment for the growth and development of industrialization, as well as increasing gender-oriented human development.

Factor analysis and countermeasures for global warming based on data science methods

In recent years, the frequent and intensifying occurrence of extreme weather events such as heatwaves, floods, and storms worldwide has led scientists and policymakers to be increasingly concerned about the phenomenon of global warming. The global average temperature has steadily risen over the past few decades, and 2015-2022 has been identified as the warmest years on record. Taking global warming as the research object, the main problem is to find out the factors affecting climate change. The article predicts the global average temperature of the future and builds a model to analyse the relationship between global average temperature, time, location and the factors affecting climate change. The prediction models of ARMIA and EEMD-LSTM were built, and the model with the best fit was obtained after several iterations of tuning and trial optimization. The results showed that Flood, Storm, and Extreme temperature, had a high correlation with the global average temperature. The data of greenhouse gas indicators such as CO2, NO, and CH4, and catastrophes such as earthquakes, Volcanic activity, and wildfires have objective effects on global warming. Random forest regression model pairs were developed, and analysis of the importance of each component to the model showed that CO2 concentration and CH4 concentration had significant effects on global average temperature. Propose measures that can curb or slow down global warming.

Optimising Plate Thickness in Interlocking Inter-Module Connections for Modular Steel Buildings: A Finite Element and Random Forest Approach

Interlocking Inter-Module Connections (IMCs) in Modular Steel Buildings (MSBs) have garnered significant interest from researchers. Despite this, the optimisation of plate thicknesses in such structures has yet to be extensively explored in the existing literature. Therefore, this paper focuses on optimising the thickness of interlocking IMCs in MSBs by leveraging established experimental and numerical simulation methodologies. The study developed various numerical models for IMCs with plate thicknesses of 4 mm, 6 mm, 10 mm, and 12 mm, all subjected to compression loading conditions. The novelty of this study lies in its comprehensive parametric analysis, which evaluates the slip prediction model. A random forest regression model, trained using the ‘TreeBagger’ function, was also implemented to predict slip values based on applied force. Sensitivity analysis and comparisons with alternative methods underscored the reliability and applicability of the findings. The results indicate that a plate thickness of 11.03 mm is optimal for interlocking IMCs in MSBs, achieving up to 8.08% in material cost reductions while increasing deformation resistance by up to 50.75%. The ‘TreeBagger’ random forest regression significantly enhanced slip prediction accuracy by up to 7% at higher force levels.