Nonlinear Regression Analysis Method Research Articles

The Effect of Optimism on Psychological Distress in Student Victims of Body Shaming

Students who are victims of body shaming can experience psychological distress, namely symptoms of depression and anxiety. So it is important to maintain psychological well-being through an optimistic mindset. The aim of this research is to find out whether there is an influence of optimism on psychological distress in students who are victims of body shaming. Sampling used an accidental sampling technique with a total of 120 respondents who were Makassar State University students who were victims of body shaming. Hypothesis testing was carried out using non-linear regression analysis methods with exponential models. The results of this research hypothesis test show that there is a negative and very significant influence between optimism and psychological distress (r=-0.628; p=0.000). The contribution of optimism to psychological distress among Makassar State University students who are victims of body shaming is 39.5%. Other findings in this research show that the three aspects of optimism, namely permanence, pervasiveness and personalization, have a negative correlation with the two aspects of psychological distress, namely depression and anxiety. The conclusion in this research is that optimism has an influence on psychological distress in students who are victims of body shaming. The higher the optimism possessed by students who experience body shaming, the lower the level of psychological distress.

Kinetic features of nonisothermal degradation of polyarylene ketones

The thermal degradation of a series of polyarylene ketones of the general formula [C6H4C(=O)-C6H4O-C6H4-R-C6H4]nwas studied by dynamic thermogravimetric analysis upon heating up to 950 K in an air flow at a heating rate of 2, 5, 10, or 20 K/min. A new method was applied for determining the activation energy of these new heat-resistant polymers degradation. The dynamics of the processes is analyzed within the framework of kinetic models, which include two or three mutually independent and non interacting stages. Using the method of nonlinear regression analysis, which includes simultaneous processing of all kinetic information obtained at different heating rates, the contributions of individual stages and their activation energies are estimated. It was shown that the activation energies of the initial steps are very large (∼ 150–390 kJ/mol) and the activation energies of the final stages are essentially lower and the same (∼ 50 kJ/mol). The results of this approach are compared with the estimates of activation energies computed by the KAS method (Kissinger-Akahira-Sunose). For polymers of a similar structure, it was found that there is a qualitative correlation between the temperatures at which a small degradation depth (2–5 %) and the activation energies of the initial stages of the process are reached.

Prediction method of blast load on underground structure surface based on neural network

The dynamic load in the soil directly leads to the damage of underground structures upon explosions. In this study, a method to predict blast load on underground structure surface based on the neural network was developed to study the load distribution under close-in detonation. First, taking the underground utility tunnel as the experimental structure, 52 groups of field blast tests were conducted on the surface load mechanism, and the surface load data samples were obtained. Second, the key influencing parameters of the reflected blast load were obtained through the dimensional analysis method, and the backpropagation neural network model was constructed based on the test data using the Levenberg–Marquardt algorithm to train and optimize the neural network. Finally, the accuracy of load prediction results was compared and evaluated among the neural network, empirical formula, and nonlinear regression analysis (NRA) methods. It is found that the input parameters of combined variables can further improve the prediction accuracy of the neural network compared with the input parameters of single physical variables. Compared with the empirical formula method and the NRA method, the neural network model with input parameters of combined variables provided the most accurate prediction. The load distribution under typical conditions calculated by the neural network showed that the explosive setting parameters impact the uneven shape of blast load on the structure surface. The increase in explosive equivalent and depth reduces the nonuniformity of load distribution, while the decrease in explosion distance increases the nonuniformity of load distribution.

Investigation on the influence of the Bohai Sea Geological Environment on the submarine stratum stability based on data mining: an intelligent prediction model.

The Bohai Sea has unique characteristics of the marine geological environment, with strong water closure and weak self-purification ability. Assessment of the submarine stratum stability plays an essential part in ocean engineering construction, preventing marine geological disasters and controlling environmental pollution for the Bohai strait. Nevertheless, limited studies have investigated quantitative classification of the submarine stratum stability or the influence of Bohai Sea's engineering geological environment on the submarine stratum stability using laboratory experiments and the data mining method. Thus, this study carried out physical and mechanical experiments based on the submarine borehole soil samples from the Bohai Sea. And a database containing 60 groups of submarine soil samples' mechanical parameters was established. The physical and mechanical parameters of the Bohai sea's submarine soil, i.e., depth, water content, void ratio, density, internal friction angle, and cohesion, are the prediction indicators. And the submarine soil samples' unconfined compressive strength and elastic modulus are used for quantifying the stability grade of Bohai Sea's submarine stratum. Furthermore, the submarine stratum stability prediction model was established based on the multiple linear regression analysis method and the nonlinear regression analysis method (i.e., BPNN, normal cloud, and TOPSIS method). The testing results indicate that the submarine stratum stability prediction model obtained by the linear regression analysis does not have obvious linear characteristics and gives a poor prediction performance (MAE=0.9333, MAPE=0.3, RMSE=1.2111). In contrast, the nonlinear analysis methods presented a higher fitting version. Compared to the cloud model and the TOPSIS method, the BPNN model with the lowest testing error (MAE=0.2, MAPE=0.1, RMSE=0.4472) presented the best prediction accuracy. The proposed submarine stratum prediction model has been validated as an effective submarine stratum stability prediction method and has good prospects for further ocean engineering applications.

ANALYSIS OF ECONOMIC AND DEMOGRAPHIC TRENDS IN ALTAI KRAI

This paper presents the analysis of the dynamics of the main economic and demographic indicators of the Altai Territory of Russia (Altai Krai) for the period 2000-2020 and defines the trends in their change, as well as their interconnectedness. The studied variables included such indicators as the number of births and deaths, arrivals and departures, average life expectancy, average per capita income, GRP, GRP per capita, etc., as well as the corresponding relative indicators. For the analysis, the methods of visual-graphical, correlation, and multivariate nonlinear regression analysis were used. More than 20 statistical indicators of the demographic and socio-economic development of the region for the period 2000-2020 were considered. As a basic model for regression analysis, a model based on the Cobb-Douglas function was applied. On the basis of correlation analysis and stepwise regression, taking into account the assessment of multicollinearity and the statistical significance of the model parameters, three factors were selected for inclusion in the econometric model, which are quite strongly and statistically significantly related to the population size: the number of people who left, the real income per capita, and GRP per capita in comparable prices. As a result of comparing the generalized indicator of the effectiveness of the influence of factors on the change in the total population with the indicator of the scale of the influence of factors on this change, it was concluded that there was a tendency for the population of the region to decrease over the study period with the increase in the scale of socio-economic factors, which indicates on the extensive development of the region's economy.

Estimation of Daily Average Global Solar Radiation with Nonlinear Regression Models Developed Using Some Meteorological and Geographical Parameters

In this study, it is aimed to develop empirical models that can be used in estimation of daily average solar radiation (RS) based on some meteorological and geographical parameters. Seven estimation models were developed by nonlinear regression analysis method using various combinations of air temperature (T), relative humidity (RH), extraterrestrial radiation (Ra), saturated (es) and actual vapour pressure (ea) parameters. The models were created using the long-term average daily meteorological data of Kahramanmaraş province (1938 – 2020). The models were tested both these long-term average data and daily meteorological data measured at Kahramanmaraş Sütçü İmam University (KSU) in 2019 and 2020. Long-term average daily actual RS data varied between 4.99 – 32.56 MJ m-2 day-1. The estimated solar radiation values (("RS" ) ̂) with the highest correlation (r = 0.99) with actual RS data were obtained with the RS_7 model, in which the parameters es, ea, T, RH and Ra were used together. The ("RS" ) ̂ values obtained using this model varied between 6.45 to 33.99 MJ m-2 day-1. For the RS_7, which showed the best performance among the seven models, mean absolute percentage error (MAPE) and root mean square error (RMSE) were determined as 4.17% and 0.69 MJ m-2 day-1, respectively. The daily RS values measured in KSU varied between 7.75 – 33.48 MJ m-2 day-1 and 10.51 – 30.23 MJ m-2 day-1 for 2019 and 2020. The ("RS" ) ̂ values closest to the measured RS values were estimated with the RS_7 model. The estimated ("RS" ) ̂ values by this model varied between 11.74 – 33.93 MJ m-2 day-1 and 13.93 – 31.57 MJ m-2 day-1 for 2019 and 2020, respectively. MAPE values were determined as 11.33% and 7.54%, respectively. It is concluded that this model can be used to estimates daily average solar radiation and will be an excellent alternative since it is compatible with the Kahramanmaraş conditions.

Crack Propagation on the Surface of the Bottleneck of a Pressure Vessel Considering the Initial Crack Angle

In order to solve the impact on the accuracy of a life prediction model when the initial crack angle of the curved surface is not 0°, the traditional stress intensity factor model is revised by the non-linear regression analysis method, and an optimized crack growth model is established. This paper takes the surface of the bottle neck of the pressure vessel as the research object, and uses the finite element method to establish a simulation model with an initial crack angle of not 0°. By analyzing the stress intensity factors of different starting angles, the relationship between the correction factor F and the pressure vessel wall thickness h, the radius of curvature R of the joint surface between the bottle neck and the bottle body, the initial crack length L and the initial angle α are calculated. The modified stress intensity factor is used to optimize the crack growth model of the pressure vessel. The experiment proves that the accuracy of the pressure vessel life prediction model is improved.

Моделирование влияния ветра на траекторию движения парашютиста

The article considers a mathematical model of a parachutist floating on the open parachute in the wind. The aim of the work is to study a system of differential equations describing the speed of the motion of the parachutist when descending on the open parachute, in order to establish the dependence of the trajectory of movement of the parachutist, the presence and stability of equilibrium states on the wind. Initially, a system of ordinary differential equations is considered, which determines the relationship between the acceleration of the parachutist and the speed along each of the three coordinates of space in windless weather. Then the influence of the wind is taken into account. The theorem on the number and stability of equilibrium states is proved. Numerical values of coefficients of the system of differential equations are obtained on the basis of real data received with the help of special software installed on the parachutist's mobile device by the method of nonlinear regression analysis. The equilibrium states of the jump, their stability and the maximum value of the speeds at the moment of landing with the presence of wind are determined. For the obtained system of ordinary differential equations, a theorem is proved on the magnitude of the skydiver's drift, the curvature and torsion of the trajectory depending on the wind.

Correlation between the Metal and Organic Components, Structure Property, and Gas-Adsorption Capacity of Metal-Organic Frameworks.

Metal-organic frameworks (MOFs) are materials in which metals and organic compounds form crystalline and porous structures. Previous studies have investigated the relationships between the structure properties and physical properties of MOFs through molecular simulations, but the overall relationships in MOFs, including the relationships between the metals and organic components and the experimentally measured physical properties, have not been clarified. In this study, we developed two regression models between three elements in MOFs: the components, structure properties, and gas-adsorption capacities as physical properties. Using a nonlinear regression analysis method, we succeeded in predicting the structure properties from the components and the physical properties from the structure properties.

Blast behavior of steel-concrete-steel sandwich panel: Experiment and numerical simulation

As a lateral resisting element, the steel–concrete composite structure has been gradually employed in the anti-seismic and the anti-explosion design of structures due to the favorable capacity of energy dissipation and impact resistance. The blast capacities of steel–concrete-steel (SCS) sandwich, concrete-steel–concrete (CSC), and reinforced concrete (RC) slabs were investigated by experiment and numerical simulation. The effects of the explosive charge, concrete grade, steel plate thickness, and length of shear studs on the blast behaviors of the composite slabs were studied by parametric analysis. The failure modes and dynamic responses of the specimens were investigated and compared. Simultaneously, the empirical formulas for mid-span deflection of the SCS and CSC slabs were proposed by using a multiple nonlinear regression analysis method. The results indicated that the three specimens exhibit different failure modes, in terms of local damage, spallation of concrete, and punching failure. And the steel plate takes a dominant role against the blast loads, which can provide a robust protective capacity to prevent the slab from damage due to the high tensile strength of steel. The SCS sandwich slab has the smallest damaged area on the front steel plate with a smaller mid-span displacement, maintains the integrity, and owns bearing capacity after the blast test compared with the CSC and RC members. The proposed empirical formula can well fit the relation among the deflection of the composite slabs, explosive charge, and thickness of steel plate, which can reasonably predict the midspan displacements of the composite slabs.

Research on the Prediction Model of Shield Advancement Rate in Sandy Cobble Ground of Beijing

The long distance and tight construction period of the earth pressure balance shield tunneling in Beijing sandy cobble ground have a very positive impact on grasping the construction progress and improving the organization efficiency. Based on the real-time monitoring data of on-site earth pressure, cutter head speed, total thrust, screw conveyor speed, and cutter head torque, this study investigated the correlation between the shield advancement rate and the above parameters based on multiple nonlinear regression analysis method, which lay down the foundation of shield advancement rate prediction. The prediction model achieved satisfactory results and was further applied to an airport project. The results show that it is of guiding significance to use multiple regression method to establish shield prediction model and put forward the best advancement parameters to ensure the construction progress. The order in which the four operational parameters affect the advancement rate is total thrust, cutter head torque, screw conveyor speed, and cutter head speed. When the advancement time is greater than 57 min, the average advancement rate and daily progress are highly positively correlated. The research provides a reliable basis for the advancement rate prediction, safety control, and parameter optimization of shield advancement in similar ground.

Support pressure assessment for deep buried railway tunnels using BQ-index

Estimation of support pressure is extremely important to the support system design and the construction safety of tunnels. At present, there are many methods for the estimation of support pressure based on different rock mass classification systems, such as Q system, GSI system and RMR system. However, various rock mass classification systems are based on different tunnel geologic conditions in various regions. Therefore, each rock mass classification system has a certain regionality. In China, the BQ-Inex (BQ system) has been widely used in the field of rock engineering ever since its development. Unfortunately, there is still no estimation method of support pressure with BQ-index as parameters. Based on the field test data from 54 tunnels in China, a new empirical method considering BQ-Inex, tunnel span and rock weight is proposed to estimate the support pressure using multiple nonlinear regression analysis methods. And then the significance and necessity of support pressure estimation method for the safety of tunnel construction in China is explained through the comparison and analysis with the existing internationally widely used support pressure estimation methods of RMR system, Q system and GSI system. Finally, the empirical method of estimating the support pressure based on BQ-index was applied to designing the support system in the China’s high-speed railway tunnel—Zhengwan high-speed railway and the rationality of this method has been verified through the data of field test.

A New Approach to Analyze the Initiated Thermal Destruction of Polycarbonate

It is established by the method of dynamic thermogravimetric analysis that bismuth dimethyldithiocarbamate (DMDTC), which protects a number of polymers from heat and light, accelerates the thermal destruction of polycarbonate (PC) based on bisphenol A and is, in this case, an initiator of the thermal destruction. The kinetics of the multistage process of the initiated thermal destruction of PC is modeled by the method of nonlinear regression analysis. The peculiarity of the proposed approach lies in estimating the parameters of the Arrhenius equation for each of the stages together for all temperature programs. It is shown that such an analysis allows us to avoid the limitations characteristic of the Kissinger (Kiss) and Kissinger–Akahira–Sunose (KAS) methods, which are traditionally used to determine the activation energies of thermal destruction of polymers.

Hydrocarbon simulation behavior of wet natural gas reservoirs

Knowing hydrocarbon dew point conditions in natural gas (HCDP) are becoming widely important in the modern marketing of liquid and gas. Without determination dew point conditions (Pressure & Temperature), the accurate description of phase changes and phase behavior cannot be achieved. Numerous models for only predicting dew point pressure of gas condensate have been proposed and other for wet natural gas reservoirs, but there is no model for predicting both dew point pressure(DPp) and dew point temperature (DPT) for wet natural gas reservoirs. Some of the published models assume knowledge the reservoir fluid composition (requiring laboratory experiments to be performed), while others only require field parameters such as reservoir temperature, stock-tank oil API, and the condensate-gas ratio (CGR). The primary objectives of this paper are to determine dew point temperature (DPT) and dew point pressure (DPp) correlations using a fluid database of nearly fifty-six wet natural gas reservoirs, that is very important for engineers to understand and manag wet natural gas reservoirs. This model was made using multiple least-square nonlinear regression analysis methods to find hydrocarbon dew point conditions (HCDP) as a function of [CGR, Tr, API gravity, γw, γC7+, MWw and C1, C2, CO2, N2 mole %] with a different constant value. Both statistical and graphical accuracy ensures that new models are more accurate in predicting hydrocarbon dew point conditions (HCDP) in comparison with equations of state by using limited data. Finally, thirty-four new separate set of measured data were used in testing models with the excellent agreement as compared with laboratory works.

Heat transfer characteristics of an 180° bend pipe of different cross sections using nano-enhanced ionic liquids (NEILs)

The fluid flow and heat transfer characteristics of ionic liquids (ILs) and nano-enhanced ionic liquids (NEILs) (i.e., alumina-based [C4mim][NTf2] and [C4mpyrr][NTf2]) through circular and non-circular (rectangular) U-bends have been numerically investigated using finite volume method of ANSYS R 16. Extensive numerical simulations have been performed to study the effects of different pertinent parameters on the heat transfer and fluid flow behaviors. Two different heat transfer correlations have been developed using the Levenberg–Marquest (L–M) method of nonlinear regression analysis by varying the influencing parameters. The Reynolds number ( $$\text{Re}$$ ), weight fraction of nanoparticles ( $$\phi$$ ) and aspect ratio (AR) (only for non-circular U-bend) have been varied in the ranges of 500–2000, 0–2.5% and 0.5–3.0, respectively. It has been observed that the Nusselt number increases with Reynolds number, weight fraction of nanoparticle dispersed in the ILs and the aspect ratio of the non-circular duct. We observe a higher heat transfer rate for the NEILs as compared to the ILs. For a particular type of NEIL and Reynolds number, we also obtain an optimum aspect ratio for a non-circular duct. The fluid flow features of the U-bend revealed two counter-rotating vortices in Y-Z planephysical properties, and quite frequently used in different at a curve angle of 90°. As the aspect ratio (for non-circular U-bend) increases up to 1.5, these counter-rotating vortices move toward the inner wall of the bend to intensify flow recirculation enhancing the heat transfer rate. We found the highest heat transfer rate for a rectangular U-bend (AR = 1.5) as compared to a square as well as a circular U-bend.

Moisture Sorption Studies of Kamsa:

The study was aimed at establishing storage stability indices of a traditional smoke dried product kamsa, produced from beef. The sample was produced using a standardized method and stored over a period of six months. Data for sorption studies was generated between the temperature ranges of 33.8oC to 50oC for adsorption and desorption using the gravimetric method. The data was analyzed using the Guggeinheim Anderson de Boer (GAB) and the Brunaeur Emmett Teller (BET) model equations. A nonlinear regression analysis method was used to evaluate the constants of the sorption equations. From the results using the GAB model, the monolayer moisture content (Mo) decreased from 0.021 to 0.008gH2O/g solids; the value of the constant K, increased from 0.587 to 1.052; and the value of CG decreased from 2.481 to 2.154. For desorption, the value of Mo decreased from 0.021 to 0.004g H2O/g solids; K increased from 0.587 to 1.035; CG increased from 2.173 to 2.646. The model gave low percent standard error values. The correlation coefficient (R) values obtained for both adsorption and desorption ranged from 0.998 to 0.999, and 0.991 to 1.000, respectively. The Mo values using the BET model at 33.8oC for both adsorption and desorption were 0.055, 0.055, 0.052, 0.049, 0.058, 0.055g H2O/g solid; and 0.057, 0.057, 0.052, 0049, 0.052, 0.057g H2O/g solid, respectively. At 50oC, the adsorption and desorption monolayer moisture values were 0.039, 0.047, 0.049, 0.049, 0.052, 0.058 gH2O/g solids; and 0.054, 0.047, 0.052, 0.052, 0.039, 0.052 gH2O/g solids, respectively. The study concluded that, the GAB model was more suitable in describing the sorption characteristics of Kamsa within the prescribed water activity and temperature ranges.

Moisture Sorption Studies of Kamsa:

The study was aimed at establishing storage stability indices of a traditional smoke dried product kamsa, produced from beef. The sample was produced using a standardized method and stored over a period of six months. Data for sorption studies was generated between the temperature ranges of 33.8oC to 50oC for adsorption and desorption using the gravimetric method. The data was analyzed using the Guggeinheim Anderson de Boer (GAB) and the Brunaeur Emmett Teller (BET) model equations. A nonlinear regression analysis method was used to evaluate the constants of the sorption equations. From the results using the GAB model, the monolayer moisture content (Mo) decreased from 0.021 to 0.008gH2O/g solids; the value of the constant K, increased from 0.587 to 1.052; and the value of CG decreased from 2.481 to 2.154. For desorption, the value of Mo decreased from 0.021 to 0.004g H2O/g solids; K increased from 0.587 to 1.035; CG increased from 2.173 to 2.646. The model gave low percent standard error values. The correlation coefficient (R) values obtained for both adsorption and desorption ranged from 0.998 to 0.999, and 0.991 to 1.000, respectively. The Mo values using the BET model at 33.8oC for both adsorption and desorption were 0.055, 0.055, 0.052, 0.049, 0.058, 0.055g H2O/g solid; and 0.057, 0.057, 0.052, 0049, 0.052, 0.057g H2O/g solid, respectively. At 50oC, the adsorption and desorption monolayer moisture values were 0.039, 0.047, 0.049, 0.049, 0.052, 0.058 gH2O/g solids; and 0.054, 0.047, 0.052, 0.052, 0.039, 0.052 gH2O/g solids, respectively. The study concluded that, the GAB model was more suitable in describing the sorption characteristics of Kamsa within the prescribed water activity and temperature ranges.

Development of a Probabilistic Seismic Performance Assessment Model of Slope Using Machine Learning Methods

The objective of this study is to propose a model that can predict the seismic performance of slope relatively accurately and efficiently by using machine learning methods. Probabilistic seismic fragility analyses of the slope had been carried out in other studies, and a closed-form equation for slope seismic performance was proposed through a multiple linear regression analysis. However, the traditional statistical linear regression analysis showed a limit that could not accurately represent such nonlinear slope seismic performances. To overcome this limit, in this study, we used three machine learning methods (i.e., support vector machine (SVM), artificial neural network (ANN), Gaussian process regression (GPR)) to generate prediction models of the slope seismic performance. The models obtained through the machine learning methods basically showed better performance compared to the models of the traditional statistical methods. The results of the SVM showed no significant performance difference compared with the results of the nonlinear regression analysis method, but the results based on the ANN and GPR showed a remarkable improvement in the prediction performance over the other models. Furthermore, this study confirmed that the GPR-based model predicted relatively accurate seismic performance values compared with the model through the ANN.

Machine learning predictive system based upon radiodensitometric distributions from mid-thigh CT images.

The nonlinear trimodal regression analysis (NTRA) method based on radiodensitometric CT images distributions was developed for the quantitative characterization of soft tissue changes according to the lower extremity function of elderly subjects. In this regard, the NTRA method defines 11 subject-specific soft tissue parameters and has illustrated high sensitivity to changes in skeletal muscle form and function. The present work further explores the use of these 11 NTRA parameters in the construction of a machine learning (ML) system to predict body mass index and isometric leg strength using tree-based regression algorithms. Results obtained from these models demonstrate that when using an ML approach, these soft tissue features have a significant predictive value for these physiological parameters. These results further support the use of NTRA-based ML predictive assessment and support the future investigation of other physiological parameters and comorbidities.

Assessing cardiovascular risks from a mid-thigh CT image: a tree-based machine learning approach using radiodensitometric distributions

The nonlinear trimodal regression analysis (NTRA) method based on radiodensitometric CT distributions was recently developed and assessed for the quantification of lower extremity function and nutritional parameters in aging subjects. However, the use of the NTRA method for building predictive models of cardiovascular health was not explored; in this regard, the present study reports the use of NTRA parameters for classifying elderly subjects with coronary heart disease (CHD), cardiovascular disease (CVD), and chronic heart failure (CHF) using multivariate logistic regression and three tree-based machine learning (ML) algorithms. Results from each model were assembled as a typology of four classification metrics: total classification score, classification by tissue type, tissue-based feature importance, and classification by age. The predictive utility of this method was modelled using CHF incidence data. ML models employing the random forests algorithm yielded the highest classification performance for all analyses, and overall classification scores for all three conditions were excellent: CHD (AUCROC: 0.936); CVD (AUCROC: 0.914); CHF (AUCROC: 0.994). Longitudinal assessment for modelling the prediction of CHF incidence was likewise robust (AUCROC: 0.993). The present work introduces a substantial step forward in the construction of non-invasive, standardizable tools for associating adipose, loose connective, and lean tissue changes with cardiovascular health outcomes in elderly individuals.