Proposed Regression Model Research Articles

Estimation of world seroprevalence of SARS-CoV-2 antibodies

In this paper, we estimate the seroprevalence against COVID-19 by country and derive the seroprevalence over the world. To estimate seroprevalence among adults, we use serological surveys (also called the serosurveys) conducted within each country. When the serosurveys are incorporated to estimate world seroprevalence, there are two issues. First, there are countries in which a serological survey has not been conducted. Second, the sample collection dates differ from country to country. We attempt to tackle these problems using the vaccination data, confirmed cases data, and national statistics. We construct Bayesian models to estimate the numbers of people who have antibodies produced by infection or vaccination separately. For the number of people with antibodies due to infection, we develop a hierarchical model for combining the information included in both confirmed cases data and national statistics. At the same time, we propose regression models to estimate missing values in the vaccination data. As of 31st of July 2021, using the proposed methods, we obtain the 95 % credible interval of the world seroprevalence as [ 35.5 % , 56.8 % ] .

Emotional showdown on social media: analyzing user reactions to the 2016 US presidential campaign

Purpose Social media platforms are highly visible platforms, so politicians try to maximize their benefits from their use, especially during election campaigns. On the other side, people express their views and sentiments toward politicians and political issues on social media, thus enabling them to observe their online political behavior. Therefore, this study aims to investigate user reactions on social media during the 2016 US presidential campaign to decide which candidate invoked stronger emotions on social media. Design/methodology/approach For testing the proposed hypotheses regarding emotional reactions to social media content during the 2016 presidential campaign, regression analysis was used to analyze a data set that consists of Trump’s 996 posts and Clinton’s 1,253 posts on Facebook. The proposed regression models are based on viral (likes, shares, comments) and emotional Facebook reactions (Angry, Haha, Sad, Surprise, Wow) as well as Russell’s valence, arousal, dominance (VAD) circumplex model for valence, arousal and dominance. Findings The results of regression analysis indicate how Facebook users felt about both presidential candidates. For Clinton’s page, both positive and negative content are equally liked, while Trump’s followers prefer funny and positive emotions. For both candidates, positive and negative content influences the number of comments. Trump’s followers mostly share positive content and the content that makes them angry, while Clinton’s followers share any content that does not make them angry. Based on VAD analysis, less dominant content, with high arousal and more positive emotions, is more liked on Trump’s page, where valence is a significant predictor for commenting and sharing. More positive content is more liked on Clinton’s page, where both positive and negative emotions with low arousal are correlated to commenting and sharing of posts. Originality/value Building on an empirical data set from Facebook, this study shows how differently the presidential candidates communicated on social media during the 2016 election campaign. According to the findings, Trump used a hard campaign strategy, while Clinton used a soft strategy.

A Machine Learning Multilayer Meta-Model for Prediction of Postoperative Lung Function in Lung Cancer Patients

The goal of this paper is to inform the machine learning community of our results obtained during the development of a system for the assessment of the postoperative lung function of patients suffering from lung cancer. The system is based on a new multilayer regression meta-model, which predicts individual postoperative forced expiratory volume in 1 second (poFEV1) for each patient based on preoperative measurements. The proposed regression models are especially trained to predict this key indicator for the 1st, 4th, and 7th day after surgery. Based on our knowledge, this is the first attempt to obtain poFEV1 in the most critical postoperative period of the first seven days. The high accuracy of the proposed predictive meta-model allows surgeons a number of key insights, starting with whether the patient is suitable for surgical intervention, and ending with the preparation of individualized postoperative treatment. It should be noted that the existing, widely used predictive models, based on functional segments (FC), Juhl-Forst, and Nakahara formulas, give two to three times worse results compared to the proposed new regression meta-model. Based on the SHapley Additive explanations (SHAP) value of the trained meta-model, it is possible to obtain a complete picture of the partial effects of each prognostic factor for each patient preoperatively on the outcome of the surgical intervention. In addition, the global model interpretation by SHAP values reveals some new interdependencies that were not known in medical circles until now. For instance, the influence of age and biomass index on the condition of the patient on the first day after surgery, or the constant significant influence of muscular support for inhalation in the entire seven-day follow-up period.

Improving solar power forecast from meteorological parameters with regression models

ABSTRACT Solar power forecasting is crucial in boosting the rivalry of solar power plants in the electricity markets and minimizing economic and social dependence on fossil fuels. This paper describes a method for foretelling solar power by utilizing regression approaches. The proposed regression models are developed using an actual meteorological set of data from Qassim University, KSA for one year (September 2021- August 2022). Forecasting solar power production is crucial to dealing with the smart grid’s demand and supply challenges. This research aims to make ML models that can precisely forecast solar power production. Significantly, the following noteworthy components highlight the main contributions of this work. Primarily a framework for the analysis of data is presented, and then the dataset obtained from the solar photovoltaic (SPV) system is visualized. Secondly, we evaluate the predictive capability of machine learning (ML) models employing numerous performance indices for predicting solar power time-series dataset values. According to the experimental findings, the proposed predictive approaches can lessen forecasting complexity even with a small reconstruction error. Additionally, the GBR (Gradient Boosting regression) model significantly outperformed other benchmark techniques in terms of forecast accuracy with MAPE of 0.7674, RMSE of 0.0191, MAE of 0.0132, MSE of 0.0030, and R2 of 0.9723 respectively.

Mathematically modeling of Ge-Sb-Te superlattice to estimate the physico-chemical characteristics

Superlattices are periodic arrangements of two or more different materials on a nanoscale. Ge-Sb-Te (Germanium-Antimony-Tellurium) superlattices are a type of material used in phase-change memory (PCM) technology. PCM is a non-volatile memory technology that relies on the reversible phase transition between amorphous and crystalline states in certain materials. This phase-change property, combined with the non-volatile nature of the material, makes it suitable for applications like phase-change memory in electronic devices. Topological and entropy indices are the mathematical tools that are helpful to estimate the physico-chemical characteristics of chemical compounds. In this article, we have calculated some reduced reverse degree-based topological indices and entropy indices for Ge-Sb-Te superlattice. Ravi et al. [32] examined the statistically significant relationship of reduced reverse degree-based topological indices with physico-chemical characteristics of drug structures and proposed regression models using entropy indices. In this context, our computed results can be helpful in predicting the physico-chemical characteristics of Ge-Sb-Te superlattices.

Modeling of well performance during oil reservoir development on the elastic-water-drive mode using regression analysis

One of the important tasks of analyzing oil field development is predicting well performance. For this purpose, displacement characteristics are often used, which represent the dependence of some indicators on others. To determine the parameters of these dependencies, regression analysis of historical data is used. Dependences of the choice of watering production wells with water pumped into injection wells, water or the law of the exhausted aquifer. A feature of displacement characteristics is generally considered to be that they can only be used when fluid flows in the formation are established. This is due to the fact that with the classical approach, displacement of characteristics is not observed in the explicit form of well interference. Therefore, the search for displacement characteristics, with the help of which we can talk about the mutual influence of wells, is an important factor. This is the subject of this work. Water cut and water-oil ratio (WOR) are related by a well-known formula. The paper proposes regression models for WOR. They obtained the result taking into account the classical logic of the WOR from accumulated oil production. Water cut is calculated from water saturation. The proposed regression models of water saturation are based on the analysis of equations of theories of two-phase filtration in difference form. 11 watering models were studied, two including classical ones and 9 new ones. Dependencies for reservoir and bottomhole pressures were also developed. The proposed models are intended to analyze the operation of wells during the development of an oil reservoir in an elastic-water-pressure mode. The models were tested on a real field and their effectiveness was analyzed. Some new models perform well in a selection of tests. In particular, all the proposed models give better results than the classical model: the logarithm of the water-oil ratio from the accumulation of oil production.

Cefixime Removal from Aqueous Solutions by Granular Ferric Oxide (GFO): Modeling and Optimization

In recent years, the presence of various pharmaceutical residues such as cefixime (CFX) in aquatic environments has been gaining attention due to its adverse effects on health and ecosystems. Since conventional treatment methods are unable to remove antibiotics, sustainable and efficient approaches are needed to remove these compounds from aquatic environments. In this study, granular ferric oxide (GFO) was used to remove CFX, and the experiments were designed using Design Expert software. The findings were then analyzed using ANOVA test. The results showed that the proposed regression model fit the experimental condition (R2=0.9701, R2 adjusted=0.9432, R2 predicted=0.83). Several residual plots were used to confirm the suitability of the model. The initial concentration of 1.84 mg/L, GFO dose of 3.05 mg/L, and contact time of 24.32 minutes were found to be the ideal conditions for CFX adsorption. Moreover, the findings showed that GFO can be effective in absorbing and removing CFX from aqueous environments.

Optimizing PEEK impact strength through multi-objective FDM 3D printing

Thermoplastic materials such as Polylactic acid, Acrylonitrile Butadiene Styrene, Polyethylene terephthalate glycol, Nylon, and Thermoplastic polyurethane are favoured in Fused deposition modeling 3D printing due to their cost-effectiveness and versatile properties. However, with the introduction of high grade thermoplastic material poses compatibility challenges with existing machines and processes, impeding widespread adoption in FDM 3D printing. Incorporating new materials into 3D printing requires adjustments to hardware, software, and settings, leading to potential expenses and time investments. Maintaining quality control and consistency becomes complex as each material demands specific parameters and processing conditions. This variability hinders achieving consistent part quality in 3D printing. Moreover, achieving optimal FDM parameters for high-grade polymers (HGPs) like Polyether ether ketone (PEEK) is a challenge due to the distinctive nature of the property, requiring specialized careful considerations during its optimization process. The considerable thermal gradient and heat distribution during printing can lead to residual stresses and deformations, significantly affecting the quality and, in particular, its impact strength. This article optimizes an industry grade 3D printing PEEK based on the limited number of process parameters, namely, build orientation, in-fill density and chamber temperature. Further, the research tries to derive a predictive model for Impact Strength (IS), which is an important consideration for the 3D printed object. In this article, along with the Impact Strength, Printing Time and Material Usage are also studied to find empirical evidence of association between these output variables or response variables. The result indicates that there is a positive significant correlation or association between them. When utilizing a specific parameter setup, the resulting IS of 86.5 kJ/m², a print time of 89 minutes, and a material usage of 3.26 grams are achieved. Notably, there is a measurable reduction of 9.18% in printing time and a 11.66% decrease in material usage when the print density is set to 100% to optimize impact strength. This optimization approach proves the use of composite desirability is a better approach where multiple objectives need to be achieved. The proposed regression model predicts the impact strength with coefficient of determination value more than 50%.

The role of the diaphragm in prediction of respiratory function in the immediate postoperative period in lung cancer patients using a machine learning model

BackgroundThe prediction of postoperative respiratory function is necessary in identifying patients that are at greater risk of complications. There are not enough studies on the effect of the diaphragm on postoperative respiratory function prediction in lung cancer surgical patients. The objective of this study is to estimate the precision of machine learning methods in the prediction of respiratory function in the immediate postoperative period and how diaphragm function contributes to that prediction.Materials and methodsOur prospective study included 79 patients who underwent lung cancer surgery. Diaphragm function was estimated by its mobility measured both ultrasonographically and radiographically and by noninvasive muscle strength tests. We present a new machine learning multilayer regression metamodel, which predicts FEV1 for each patient based on preoperative measurements.ResultsThe proposed regression models are specifically trained to predict FEV1 in the immediate postoperative period and were proved to be highly accurate (mean absolute error in the range from 8 to 11%). Predictive models based on resected segments give two to three times less precise results. Measured FEV1 was 44.68% ± 14.07%, 50.95% ± 15.80%, and 58.0%1 ± 14.78%, and predicted postoperative (ppo) FEV1 was 43.85% ± 8.80%, 50.62% ± 9.28%, and 57.85% ± 10.58% on the first, fourth, and seventh day, respectively. By interpreting the obtained model, the diaphragm contributes to ppoFEV1 13.62% on the first day, 10.52% on the fourth, and 9.06% on the seventh day.ConclusionThe machine learning metamodel gives more accurate predictions of postoperative lung function than traditional calculations. The diaphragm plays a notable role in the postoperative FEV1 prediction.

Analyzing County-Level COVID-19 Vaccination Rates in Texas: A New Lindley Regression Model

This work aims to study the factors that explain the COVID-19 vaccination rate through a generalized odd log-logistic Lindley regression model with a shape systematic component. To accomplish this, a dataset of the vaccination rate of 254 counties in the state of Texas, US, was used, and simulations were performed to investigate the accuracy of the maximum likelihood estimators in the proposed regression model. The mathematical properties investigated provide important information about the characteristics of the distribution. Diagnostic analysis and deviance residuals are addressed to examine the fit of the model. The proposed model shows effectiveness in identifying the key variables of COVID-19 vaccination rates at the county level, which can contribute to improving vaccination campaigns. Moreover, the findings corroborate with prior studies, and the new distribution is a suitable alternative model for future works on different datasets.

A data-driven operating improvement method for the thermal power unit with frequent load changes

In the near and medium term, thermal power generation will still play an important peak-shaving role in providing grid connection to volatile renewable energy. Thermal power units need to adjust from the current load to the given load within a given time period, which provides space for operational improvement. In this paper, we propose an operating improvement method for the thermal power unit based on real-time monitoring data by observation dividing, feature construction and selection, clustering, and machine learning. The unit operation data is categorized into three feature subsets based on domain knowledge, which is used to distinguish different functions of historical data. Subsequently, the optimal feature subset and observations are selected for building regression models, including linear regression (LR), ensemble tree regression (ETR), neural network regression (NNR), and support vector regression (SVR). R-squared (R2), root mean square error (RMSE), and mean absolute error (MAE) are adopted to test the performance of the proposed regression model on the real-time monitoring data of a thermal unit, which has 80,000 observations of 36 different variables. Compared with benchmark methods, the proposed method has lower regression error in the numerical experiment. Thus, we can thereby improve the efficiency of operational management based on the built learning model. Furthermore, in response to peak shaving requirements, the proposed method in this article considers operational optimization over time periods containing multiple time points compared to the traditional operational optimization perspective. With the continuous arrival of monitoring data, the above method can be updated in a timely manner based on the new database to address model adjustments caused by unit aging and other factors.

Unit upper truncated Weibull distribution with extension to 0 and 1 inflated model – Theory and applications

A two-parameter unit distribution and its regression model plus its extension to 0 and 1 inflation is introduced and studied. The distribution is called the unit upper truncated Weibull (UUTW) distribution, while the inflated variant is called the 0−1 inflated unit upper truncated Weibull (ZOIUUTW) distribution. The UUTW distribution has an increasing and a J-shaped hazard rate function. The parameters of the proposed models are estimated by the method of maximum likelihood estimation. For the UUTW distribution, two practical examples involving household expenditure and maximum flood level data are used to show its flexibility and the proposed distribution demonstrates better fit tendencies than some of the competing unit distributions. Application of the proposed regression model demonstrates adequate capability in describing the real data set with better modeling proficiency than the existing competing models. Then, for the ZOIUUTW distribution, the CD34+ data involving cancer patients are analyzed to show the flexibility of the model in characterizing inflation at both endpoints of the unit interval.

New Partially Linear Regression and Machine Learning Models Applied to Agronomic Data

Regression analysis can be appropriate to describe a nonlinear relationship between the response variable and the explanatory variables. This article describes the construction of a partially linear regression model with two systematic components based on the exponentiated odd log-logistic normal distribution. The parameters are estimated by the penalized maximum likelihood method. Simulations for some parameter settings and sample sizes empirically prove the accuracy of the estimators. The superiority of the proposed regression model over other regression models is shown by means of agronomic experimentation data. The predictive performance of the new model is compared with two machine learning techniques: decision trees and random forests. These methods achieved similar prediction performance, i.e., none stands out as a better predictor. In this sense, the objective of the research is to choose the best method. If the objective is only predictive, the decision tree can be used due to its simplicity. For inference purposes, the regression model is recommended, which can provide much more information regarding the relationship of the variables under study.

Robust design modeling and optimization for dealing with a non-convex design space

Classic experimental designs effectively generate experimental design points under standard design spaces such as a circle, a cube, or a sphere. However, for non-convex design spaces, traditional experimental designs are not appropriate due to nonstandard experimental design conditions. No attention has been given to developing an algorithm to generate design points for a non-convex design space in the literature. This paper is three-fold. One, this paper presents a novel exchange algorithm based on an inner approximation approach in order to construct optimal design points where the design space is non-convex. Besides, the proposed algorithm efficiently produces experimental design points for non-convex design space. In addition, it is provided a fair comparison of the proposed algorithm performance. Two, the fitted response functions of the mean, standard deviation, and variance are found using the proposed regression model construction technique where the design space is non-convex. Three, a selected optimal design concept-based robust design optimization model is developed to find Pareto optimal solution settings of chosen design factors for the non-convex design space. Finally, an Industry 4.0 concept-based numerical example is provided to illustrate how to apply the proposed methodology effectively for the non-convex design space.

Regression models for circular data based on nonnegative trigonometric sums

The parameter space of nonnegative trigonometric sums (NNTS) models for circular data is the surface of a hypersphere; thus, constructing regression models for a circular-dependent variable using NNTS models can comprise fitting great (small) circles on the parameter hypersphere that can identify different regions (rotations) along the great (small) circle. We propose regression models for circular- (angular-) dependent random variables in which the original circular random variable, which is assumed to be distributed (marginally) as an NNTS model, is transformed into a linear random variable such that common methods for linear regression can be applied. The usefulness of NNTS models with skewness and multimodality is shown in examples with simulated and real data.

The simplex regression model as a methodology of actuarial analysis

The risk management business evolves rapidly, so actuaries are faced with the need for new analysis methodologies. However, the use of incorrect methodologies for actuarial modeling can have serious implications for strategic decision making. This study aims to introduce the simplex regression model as a suitable methodology for actuarial modeling of data whose values belong to the unit interval. Using a risk management data set, the linear model with normal distribution and the proposed regression model were compared. The evaluation of the models presented concluded by the quality of the modeling through simplex regression, indicating the quality of this method as a new analysis tool for the actuarial context.

Influence of the Transverse Stress Component of Ferromagnetic Materials on MBN Signals Under Bidirectional Stress Conditions

This paper studies the effect of the transverse stress component of magnetically isotropic ferromagnetic materials on the magnetic Barkhausen noise (MBN) testing signals under planar bidirectional stress states. Based on the logistic distribution function, a model that can be used to describe the effect of the planar bidirectional stress level on MBN characteristic signals is proposed; the formulated model works within the elastic limit of the magnetically isotropic ferromagnetic materials. Then, the characteristics of the proposed regression model are analyzed by computer simulation. Finally, to validate the proposed regression model, the bidirectional stress experimental data extracted from the published paper is used. The results show that there is a high linear correlation between the actual data and the predicted data of the formulated model. Therefore, it can be proved that the proposed planar bidirectional stress regression model based on the logistic distribution function is feasible, applicable, and valuable.

A Novel Methodology for Estimating Technology Value and Importance of Factors in Market-Based Approach

Technology valuation methods are classified into income-based, cost-based, and market-based approaches depending on the perspective of valuing technology. The market approach evaluates the value of technology by referring to cases in which similar technologies have been traded before. In this study, we use prior technology transaction data to estimate the technology value based on the market approach and to identify influential factors to the estimated value. To this end, we adopt a multivariate k-nearest neighbor (MKNN) regression model to accommodate mixed-type input variables aiming at estimating multivariate technology values, selecting influencing factors, and the relative importance of the selected factors. In addition, we can optimize the number of transaction cases k in k-NN regression. Our proposed regression model outperforms an embedding model with cosine similarity in predicting multivariate response variables. In addition, we illustrate how to select and assess the influential factors based on the real-life dataset.

Anomalous NO[formula omitted] emitting ship detection with TROPOMI satellite data and machine learning

Starting from 2021, more demanding NOx emission restrictions were introduced for ships operating in the North and Baltic Sea waters. Since all methods currently used for ship compliance monitoring are financially and time demanding, it is important to prioritize the inspection of ships that have high chances of being non-compliant. The current state-of-the-art approach for a large-scale ship NO2 estimation is a supervised machine learning-based segmentation of ship plumes on TROPOMI/S5P images. However, challenging data annotation and insufficiently complex ship emission proxy used for the validation limit the applicability of the model for ship compliance monitoring. In this study, we present a methodology towards the automated and scalable selection of potentially non-compliant ships using a combination of machine learning models on TROPOMI satellite data. It is based on a proposed regression model predicting the amount of NO2 that is expected to be produced by a ship with certain properties operating in the given atmospheric conditions. The model does not require manual labeling and is validated with TROPOMI data directly. The differences between the predicted and actual amount of produced NO2 are integrated over observations of the ship in time and are used as a measure of the inspection worthiness of a ship. To add further evidence, we compare the obtained results with the results of the previously developed segmentation-based method. Ships that are also highly deviating in accordance with the segmentation method require further attention. If no other explanations can be found by checking the TROPOMI data, the respective ships are advised to be the candidates for inspection.

The Pezeta regression model: an alternative to unit Lindley regression model

A new probability distribution is proposed in this paper. This new distribution has support on the interval and was obtained after transforming the random variable with exponential distribution. The mode, quantile function, median, ordinary moments and density function belongs to exponential family of distributions are demonstrated. The maximum likelihood method is used to obtain the parameter estimate. A regression model for the median of the distribution is also proposed. Closed-form expressions for the score vector and Fisher’s information matrix are demonstrated. A simulation study and an application to real data showed the good performance of the proposed regression model.