Polynomial Regression Research Articles

Effects of Mango Seed (Mangifera indica) Powder on Growth Performance, Immune Response, Gut Morphology, and Gene Expression of Nile Tilapia (Oreochromis niloticus)

This study explored the effects of mango seed (MS) powder supplementation on the growth, immune response, gene expression, and intestinal morphology of Nile tilapia (Oreochromis niloticus) over an 8-week period. A total of 300 Nile tilapia fingerlings (average weight of 15.29 ± 0.05 g) were divided into five treatment groups and fed either a basal diet or one of four experimental diets containing MS powder at concentrations of 10 (MS10), 20 (MS20), 40 (MS40), and 80 (MS80) g kg−1. The results demonstrated that Nile tilapia fed MS-supplemented diets experienced significant improvements (p < 0.05) in weight gain (WG), specific growth rate (SGR), and survival rate (SR) compared to the control group (0 g kg−1 MS). The MS-treated groups also showed a significant increase (p < 0.05) in the height and branching of intestinal villi along the entire length of the intestine, as well as a significantly higher villus-to-crypt depth ratio (V/C), indicating enhanced intestinal health and functionality. Moreover, although MS supplementation did not increase peroxidase activity, it did lead to a significant increase (p < 0.05) in the activity of skin mucus and serum lysozyme, along with upregulated gene expression of immune-related (IL-1, IL-8, and LBP) and antioxidant genes (GST-α, GPX, and GSR). Polynomial regression analysis identified an optimal MS dosage of 36.43–45 g kg−1 for effectively improving growth, immunity, and immuno-oxidant gene expression in Nile tilapia. These results emphasize mango seed (MS) as a promising natural supplement for improving the diet of Nile tilapia and, potentially, other freshwater fish widely used in aquaculture.

Data-driven analysis of bonding strength in laser-structured metal-GFRP hybrid joints via groove morphology

Abstract This study aims to enhance predictions of the mechanical properties of mechanically interlocked hybrid joints by employing machine learning techniques coupled with feature engineering of cross-sectional groove morphology. Unlike mechanical fastening, which promotes localized stress, and adhesive bonding, which requires prolonged contaminant removal, mechanically interlocked joints offer a distinct advantage by eliminating the need for either. The mechanically interlocked joints in this study combine glass fiber reinforced composite fabricated via injection molding, with cold rolled steel structured by a nanosecond laser. Through optical microscopy, crucial groove dimensions such as depth and width are identified for feature extraction. Domain-specific feature engineering is employed to improve predictive accuracy, integrated with existing regression models. The concept of “structure density,” initially defined as groove width over hatch distance, is expanded during feature engineering to include additional relevant features over hatch distance. Experimental investigations identified optimal laser parameters for shear strength, yielding a maximum single lap shear strength of 33.3 MPa under specific conditions. The third polynomial regression model incorporating structure density features emerged as the most effective in predicting shear strength, demonstrating high accuracy in both interpolation and extrapolation scenarios. The study suggests potential cost savings by utilizing surface topography for shear strength prediction, with implications for industries amidst the increasing prevalence of composite materials.

A stochastic analysis and bibliometric analysis of COVID-19

COVID-19 a novel Corona Virus Disease which was caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome Corona Virus 2) continues to pose a critical and urgent threat to global health. When an infected person comes in contact with a normal individual or when the infected person sneezes or coughs, the virus that triggers COVID-19 spreads.The spread of novel SARS-CoV-2 was increasing, and the threats caused by it were becoming more severe in 2021. To counter the disease and save countless lives in danger, it is necessary to predict the trend of the number of cases and deaths and then implement the policies accordingly. In this paper, the trends of the growth rate of worldwide cases and deaths were studied, and the future growth for 100 days was predicted using the Neural network model and Polynomial Regression model. For efficient planning, the countries were grouped using Principal Component Analysis and the predictions were made. The cases and deaths in different countries and states were related through the Pearson coefficient, and the heat maps were studied. Additionally, in this paper, a case study to predict the trend of cases, number of deaths and recoveries in India was also performed. The Indian states were grouped into four groups based on the Principal Component Analysis (PCA) results, and relevant remarks and trends were suggested. The growth of cases and deaths was studied, and the peaks were predicted for the next 200 days. In recent months, COVID-19 has generated a significant deal of anxiety as a global pandemic, and an increasing number of studies have been published in this area. Consequently, a bibliometric examination of these papers may offer insight into current research hot subjects and trends. We are the first to join stochastic analysis of COVID-19 effects with bibliometric analysis of COVID-19. This prediction, if taken into consideration strategically during the planning of preventive measures of COVID-19 can help to reduce the cases to a great extent.

Decoding continuous motion trajectories of upper limb from EEG signals based on feature selection and nonlinear methods.

Brain-computer interface (BCI) system has emerged as a promising technology that provides direct communication and control between the human brain and external devices. Among the various applications of BCI, limb motion decoding has gained significant attention due to its potential for patients with motor impairment to regain independence and improve their quality of life. However, the reconstruction of continuous motion trajectories in BCI systems based on electroencephalography (EEG) signals remains a challenge in practical life. This study investigates the feasibility of applying feature selection and nonlinear regression for decoding motion trajectory from EEG. We propose to fix the time window, select the optimal feature set, and reconstruct the motion trajectory of motor execution tasks using polynomial regression. The proposed approach is validated on a public dataset consisting of EEG and hand position data recorded from 15 subjects. Several methods including ridge regression and multiple linear regression are employed as comparisons. The cross-validation results show that the proposed reconstructed method has the highest correlation with actual motion trajectories, with an average value of 0.511±0.019 (p<0.05). This finding demonstrates the great potential of our approach for real-world motor kinematics BCI applications.

Specific Heat Analysis of Water and Ethylene Glycol Based rGO-ND Hybrid Nanofluids: Experimental and Artificial Neural Network Predictions

The isobaric specific heat was measured experimentally for two kind of hybrid nanofluids like water and ethylene glycol based reduced graphene oxide-nanodiamond (rGO-ND) hybrid nanofluids at different particle volume loadings of 0.2%, 0.4%, 0.6%, 0.8% and 1.0%, and in the temperature range from 293 K to 333 K, respectively. The obtained experimental specific heat data was used for the artificial neural network (ANN) algorithms of Support Vector Regression (SVR), and Levenberg-Marquardt (LM) models for the predictions. Results indicated that, the specific heat of water, and ethylene glycol-based hybrid nanofluids at 1.0% vol. of hybrid nanofluid is lowered by 1.09% and 1.10% at a temperature of 333 K, compared to their own base fluids. The SVR and LM models for the specific heat of water-based hybrid nanofluids predict accurately with a correlation coefficient of 0.99849, and 0.99957, similarly, the SVR and LM models for the specific heat of ethylene glycol-based hybrid nanofluids predict accurately with a correlation coefficient of 0.99998, and 0.99906, respectively. The obtained data was compared with other kind of nanofluids data. The polynomial regression equation was proposed for the water and ethylene glycol-based hybrid nanofluids through the SVR model.

Spatiotemporal changes in the congestion index of streets and roads in the armed conflict conditions

This article examines the impact of war on the formation of urban transport flows. During armed conflicts, the transport infrastructure of cities undergoes significant changes, which greatly affects the mobility and safety of the population. The need to study this issue is particularly relevant in the context of the ongoing Russian-Ukrainian war, which has caused the largest migration in Europe since World War II. The paper explores the dynamics of these changes and ways to adapt urban transportation systems to war conditions. The study aims to determine the parameters of urban transport zones with specific disruptions in network link congestion indices during different phases of the full-scale invasion of Ukraine by the Russian Federation. The research methodology is based on analyzing statistical data on population movements, applying traffic flow models, and conducting a systematic analysis of the interaction between various components of urban transport systems. The goal of this study is to establish the relationships between the areas of cities where disruptions in congestion indices were observed during the initial phase of the invasion. The cities studied are Lviv and Kyiv, whose road networks are also described in the article. Polynomial regression models with two independent variables (the congestion index and the number of days from the beginning of each phase) were developed for three predefined time phases, each with distinct features of the armed conflict. The dependent variable is the area of the city experiencing disruptions in the congestion index relative to normal traffic flow conditions. The study concludes that the relationship between changes in the congestion index and the area of the city experiencing deviations is directly proportional. The absolute values of the indicators studied are lower for Lviv’s network than for Kyiv’s.

Data-driven model for marine engine fault diagnosis using in-cylinder pressure signals

Effective diagnosis of marine engines that is crucial for safe and reliable ship operations requires fidel tools for the identification of critical faults. However, the unavailability of extensive measured data-sets corresponding to engine faulty conditions renders the development of such tools challenging. This study aims to develop a data-driven fault estimation model considering information extraction methods and regression techniques of low computational effort, namely multiple linear and polynomial regression. The required data-sets for healthy and faulty conditions for four critical faults and their combinations are generated by employing a calibrated zero-dimensional thermodynamic model representing a marine four stroke medium speed diesel engine, which is validated against engine shop trial measurements. Fourier analysis of the derived in-cylinder pressure profiles is employed to calculate the coefficients of the harmonic orders. Several harmonics coefficients sets are used as input to the regression models to estimate the severity of the four considered faults. The results demonstrate that initial 20 harmonics are sufficient to effectively estimate the severity for each fault, whereas polynomial regression is highly effective, exhibiting R 2 values greater than 98 % . This study provides insights on the data-driven simultaneous faults severity estimation, and as such it impacts the advancement of cost-effective diagnostic methods for marine engines.

Cointegrating Polynomial Regressions With Power Law Trends

Cointegrating Polynomial Regressions With Power Law Trends

Can government green discourse-behavior congruence mitigate carbon emissions? A polynomial regression with response surface analysis

As global climate change intensifies, the efficacy of governmental environmental policies and their implementation is pivotal for carbon emission control. The alignment between governmental green initiatives and their actions not only affects authority credibility but also determines the success of environmental governance outcomes. Therefore, investigating the congruence between government green discourse and behavior in achieving “dual carbon” objectives is critical for addressing climate change and facilitating systemic reforms. Applying panel data from 30 provinces in China from 2012 to 2022 based on polynomial regression and response surface, this study summarizes three main pathways through which government green discourse-behavior congruence influences carbon emissions: the state of congruence or incongruence, the degree of congruence, and the direction of incongruence. Congruent government green discourse and behavior more effectively reduce carbon emissions intensity than incongruent ones, while a higher degree of congruence leads to more significant reductions. The direction of incongruence also impacts carbon emissions intensity, particularly when government behaviors fall short of its discourses, leading to a higher carbon intensity growth rate. Furthermore, the moderating role of non-governmental entities' green innovation vitality is considered. The results reveal that the green innovation of non-governmental entities can partly substitute for the need for government discourse-behavior congruence in achieving carbon reduction. Specifically, green innovation of research institutions primarily enhances the positive environmental effects of increased government discourse-behavior congruence degree, while corporate green innovation mainly mitigates the negative environmental impacts of government incongruence direction. Consequently, the deep integration of government environmental initiatives and actions is advocated, along with the stimulation of green innovation among various entities, to more effectively combat global climate change. This study not only extends traditional congruence research from the micro level to the meso and macro levels and fills a theoretical gap, but also provides new insights into the process of government green governance worldwide.

Dancing in Tandem: The Role of HR Value Congruence and Line Manager‐HR Manager Collaboration in Effective HR Implementation

ABSTRACTDespite the increasing significance of partnerships between line managers and human resource managers in implementing HR practices, prior research has overlooked the potential interplay between these two groups in terms of collaboration and line managers' subsequent HR implementation behavior. By integrating insights from value congruence theory and social context theory, we formulated a model that examines how congruence or incongruence in HR values between line managers and HR managers influences line managers' HR implementation behaviors through their collaboration. We tested our hypotheses using multi‐source and multi‐wave data from 206 stores within a large retail chain organization (206 line managers, 206 HR managers, and 1227 employees). The polynomial regression results revealed that optimal collaboration and line managers' HR implementation behaviors occur when both parties agree on HR value, while disagreement negatively impacts these aspects. Moreover, we observed significant differences between the two levels of agreement (i.e., “high–high” congruence vs. “low–low” congruence). We further found that HR managers' political skill moderates the relationship between incongruence in HR value and collaboration levels. This research advances the understanding of HR implementation by emphasizing the importance of mutual agreement between line managers and HR managers and the role of HR managers' political skill.

Using Differential Scanning Calorimetry to Measure the Energetic Properties of Residual Sludge and Catalysts from the Textile, Tannery, and Galvanic Industries

This research delved into the energetic properties of catalysts synthesized from residual sludge from the textile, galvanic, and tannery industries. The experimental process consisted of an initial heat treatment to activate their catalytic properties and a thermal analysis employing differential scanning calorimetry (DSC). This technique permitted the investigation of the materials’ thermal behavior as a function of temperature, ranging from 142 to 550 °C, effectively controlling the heating rates and pressure conditions. The data gathered were the input for constructing specific heat models through polynomial regression employing the least squares method. These models were subsequently used to estimate variations in the enthalpy and entropy for both the sludge and catalysts through integration. Third-degree polynomials primarily characterized the specific heat models that accurately represented the samples’ thermal behavior, considering variations in their physicochemical properties that influenced it. The catalysts derived from residual sludge from the textile industry exhibited the models with the most robust statistical fit. Concurrently, the catalysts from the galvanic industry displayed noteworthy similarities with the bibliographic data across various temperature points. The mathematical models determined the specific heat (Cp) as a function of temperature, which, in turn, was used to estimate the enthalpy and entropy variations in the sludge and catalysts under study. The highest enthalpy value corresponded to the sludge and catalyst obtained from the tannery industry, with a Cp of 5.60 J/g-K at 603 K and 2.45 J/g-K at 445.6 K. Finally, the third-degree polynomials showed the best mathematical models since (1) they considered the variations in the physicochemical properties that intervened in the behavior of Cp as a function of temperature; (2) they presented a better statistical fit; and (3) they showed consistency with the existing information in the literature for the textile industry and the galvanic industries.

Prediction of Higher Education Student Dropout based on Regularized Regression Models

This study explores the critical topic of student dropout in higher education institutions. To allow early and precise interventions and to provide a multifaceted view of student performance, this study combined two predictive models for dropout classification and score prediction. At first, a logistic regression model was developed to predict student dropout at an early stage. Then, to enhance dropout prediction, a second-degree polynomial regression model was used to predict student results based on available academic variables (access, tests, exams, projects, and assignments) from a Moodle course. Dealing with a limited dataset is a key challenge due to the high risk of overfitting. To address this issue and achieve a balance between overfitting, data size, and model complexity, the predictive models were evaluated with L1 (Lasso) and L2 (Ridge) regularization terms. The regularization techniques of the predictive models led to an accuracy of up to 89% and an R2 score of up to 86%.

Enhancement of groundwater resources quality prediction by machine learning models on the basis of an improved DRASTIC method

Determining situation of groundwater vulnerability plays a crucial role in studying the groundwater resource management. Generally, the preparation of reliable groundwater vulnerability maps provides targeted and practical scientific measures for the protection and management of groundwater resources. In this study, in order to evaluate the groundwater vulnerability of Kerman–Baghin plain aquifer, two developed indicators including composite DRASTIC index (CD) and nitrate vulnerability index (NVI) based on DRASTIC index were considered. Soft computing methods, including Gene Expression Programming (GEP), Evolutionary Polynomial Regression (EPR), Multivariate Adaptive Regression Spline (MARS), and M5 Model Tree (MTM5) have been used to provide formulations for prediction of NVI. Soft computing techniques were fed nine input parameters: depth to water level, net recharge, aquifer environment, soil environment, topography, effect of unsaturated area, hydraulic conductivity, land use, and potential risk related to land use. After calculating the vulnerability by soft computing methods, the results showed that the EPR model with Correlation Coefficient (R) of 0.9999 and Root Mean Square Error (RMSE) = 0.2105 has the best performance in the testing stage in comparison with MARS (R = 0.9966 and RMSE = 2.408), M5MT (R = 0.9956 and RMSE = 2.988), and GEP (R = 0.9920 and RMSE = 3.491). Although the EPR and GEP models have more complex mathematical computations than other soft computing models, the MARS and MT model that have quadratic polynomial and multivariable linear structures respectively, can be considered as the best alternative. According to the MARS model, the vulnerability of the region is divided into two categories: very low vulnerability (73.06%) and low vulnerability (26.94%). Overall, the statistical results of soft computing techniques were indicative of effective formulations for evaluating the DRASTIC index.

Comparing Moving Averages And Polynomial Regression In Financial Trend Analysis

Trend analysis forms the backbone of studies related to financial markets as they give investors and market participants insights into expected market behavior, aiding in investment decisions. This paper compares two methods for determining financial trends: Moving Averages (MAs) and Polynomial Regression (PR). Widely used technical indicators like Simple Moving Averages (SMA) and Exponential Moving Averages (EMA) are popular due to their ease of understanding and effectiveness in smoothing price data by removing noise. Although simpler models like linear or logistic regression can be used if the data is suitable, polynomial regression is considered more flexible as it fits a non-linear curve to the data. This study aims to examine the effectiveness of these two approaches in detecting and predicting financial trends accurately. The research involves calculating SMA and EMA on historical stock price data and applying polynomial regressions of various degrees. A comprehensive comparison will focus on the fit, precision, and predictability of each method in financial trend analysis

A Novel Fitting Polynomial Approach for An Accurate Soc Estimation In Li-Ion Batteries Considering Temperature Hysteresis

Lithium-ion batteries are essential to modern technology, requiring accurate estimation of the state of charge (SOC) for optimal performance. Traditional methods such as Coulomb Counting (CC) are ineffective in the face of temperature variations, leading to inaccuracies in SOC estimation, which in turn cause obvious deformation of hysteresis curves. To address this, this paper introduces a novel method called Polynomial Fit State of Charge (FPSOC), for effective SOC estimation. This method incorporates a fifth-degree polynomial fitting that accounts for a wide range of temperature variations (from -10°C to +80°C), a feature that, according to the authors, has not been offered by all previously published methods. A series of simulation tests using the MATLAB/Simulink tool are conducted under various temperature profiles to evaluate the effectiveness of the FPSOC method. The results demonstrate the notable superiority of the FPSOC model compared to the CC method, with a significantly reduced RMSE of only 0.93% compared to 6.77% of the CC model. Particularly effective at low SOC levels (30%), the FPSOC model demonstrates precision up to six times higher compared to the CC model. Additionally, when evaluated against other recent SOC estimation techniques such as CM, RLSF, EKF, DST, BBDST, ASMO, LPM_H, LSTM-SA Group A and B, and baseline ECM-ID, The FPSOC method proves extremely accurate, with the lowest average error under different temperature conditions.

Estimating Drone Visual Line-of-Sight Distance Using Machine Learning Approaches

In this study, we conducted flight tests to establish a clear standard for the visual line-of-sight (VLOS) distance of drones using machine learning models, as outlined in the Aviation Safety Act. Various machine learning models were applied and compared to predict the VLOS distance based on flight data. The analysis revealed that factors such as flight altitude, drone size, and observer’s vision significantly influence the VLOS distance. In particular, drone volume and observer’s vision were identified as the most important factors in predicting VLOS distance. The Random Forest Regression model demonstrated the best predictive performance, followed by the Polynomial Regression model. This study provides fundamental data to ensure safe drone operations and compliance with aviation regulations. The findings can also serve as practical resources for drone operators in planning safe flights. Future works should focus on collecting data from diverse environmental conditions to improve the generalization of prediction models. Additional research is also needed on beyond visual line-of-sight (BVLOS) and night flights, as these are critical areas for drone commercialization and require new predictive models and technological advancements to ensure safety.

Modeling and Simulation of Vehicle Velocity-Density on Buah Batu Road Using Second-Order Polynomial Regression

The problem of traffic density is a complex problem in the world of land transportation, especially in urban areas, including Bandung City. Buah Batu Road, one of the main roads in Bandung City which is 13 meters wide and 1.70 kilometers long, connecting Bandung City and Bandung Regency. And this study examines the relationship between vehicle speed and traffic density on Buah Batu Road, Bandung. Using the macroscopic Lighthill-Whitham Richards (LWR) model, Second Order Polynomial Regression, and Lax-Wendroff scheme simulation. This study aims to obtain the speed-density function for traffic. The introduction emphasizes the importance of understanding traffic flow dynamics to reduce congestion, especially in areas with significant vehicle growth. The methodology used is direct observation of the Buah Batu Road section with an observed length of 18 meters, with data collected through cellphone camera recordings at various times. These observation data provide insight into vehicle density and speed under various conditions.

Modal Matching of Key Components in the Launching System Based on Response Surface Method

This chapter adopts Response Surface Methodology (RSM) combined with dynamic simulation and statistical analysis to deeply investigate the impact of modal matching of the inherent frequencies of key components of the launching system on the disturbances at the launch box opening during firing. Through experimental design, a polynomial fitting response surface model is established between design parameters and response objectives. The ultimate goal is to find the optimal design scheme that minimizes the initial disturbance, thereby achieving effective modal matching between the main components of the launching device. This research aims to provide a theoretical basis for improving firing accuracy and system stability.

Empathic disequilibrium in schizophrenia: An individual participant data meta-analysis

Schizophrenia involves substantial social difficulties, yet their nature remains unclear. Although empathy has been considered a promising social cognition construct, inconsistent findings have undermined its usefulness as a stable index for schizophrenia. This may be because previous studies overlooked the interdependency between the emotional and cognitive components of empathy. In this study, we investigated whether empathic disequilibrium, the intrapersonal imbalance between emotional and cognitive empathy, could be a meaningful schizophrenia marker. We conducted an individual-participant data meta-analysis, systematically searching the literature for studies involving participants with schizophrenia who completed the Interpersonal Reactivity Index, a validated empathy measure. Using emotional and cognitive empathy to capture empathic disequilibrium and the joint effect of cognitive and emotional empathy, we employed polynomial regression with response surface analysis to predict schizophrenia diagnosis and symptoms. Our analysis comprising ten studies (N = 1,080), revealed a non-linear association with the joint effect of cognitive and emotional empathy, as well as an association with empathic disequilibrium, suggesting emotional empathy overabundance, strongly and consistently predicted schizophrenia diagnosis. Additionally, empathic disequilibrium towards cognitive empathy overabundance was related to greater positive symptoms. The results suggest that empathic disequilibrium provides a stable behavioral marker related to schizophrenia, surpassing the utility of empathy alone. The findings deepen our understanding of schizophrenia phenomenology and can advance clinical and research practices.

Optimizing yaw angles for improved power generation in offshore wind farms: A statistical approach

Aerodynamic interactions among wind turbines diminish power generation in offshore wind farms. Adjusting a turbine’s yaw angle, deliberately misaligned from the wind direction, mitigates energy losses from wake effects, thereby enhancing overall power generation. This study employs advanced wind farm simulation software for numerical simulations to compute the optimal yaw angle and associated percentage power gain for three offshore wind turbines under varying conditions, encompassing turbine models, wind speeds, turbulence intensities, and layouts. Two polynomial regression models and one decision tree classification model are developed to estimate the yaw angle and percentage power gain based on these conditions. These models are computationally efficient, integrating previously unconsidered predictors, and facilitating assessment of predictor impacts on yaw angle and power gain. Moreover, they enable real-time adjustment of turbine nacelle direction, positioning them for effective deployment at scale in offshore wind farms. Implementing these models is anticipated to extend and facilitate the use of turbine yawing as a strategy to enhance energy generation, providing computationally efficient tools for optimizing power generation in ocean wind farms.