Multiple Nonlinear Regression Model Research Articles

Sensitivity of daily reference evapotranspiration to weather variables in tropical savanna: a modelling framework based on neural network

Accurate prediction of reference evapotranspiration (ETo) is crucial for many water-related fields, including crop modelling, hydrologic simulations, irrigation scheduling and sustainable water management. This study compares the performance of different soft computing models such as artificial neural network (ANN), wavelet-coupled ANN (WANN), adaptive neuro-fuzzy inference systems (ANFIS) and multiple nonlinear regression (MNLR) for predicting ETo. The Gamma test technique was adopted to select the suitable input combination of meteorological variables. The performance of the models was quantitatively and qualitatively evaluated using several statistical criteria. The study showed that the ANN-10 model performed superior to the ANFIS-06, WANN-11 and MNLR models. The proposed ANN-10 model was more appropriate and efficient than the ANFIS-06, WANN-11 and MNLR models for predicting daily ETo. Solar radiation was found to be the most sensitive input variable. In contrast, actual vapour pressure was the least sensitive parameter based on sensitivity analysis.

Design of melting parameters for safety airbag labels based on hot air welding technology: CFD simulation and experimental validation

This paper focuses on researching the various factors that influence the material melting rate during the hot air melting process. To achieve this purpose, the paper proposes a melting model for Acrylonitrile-Butadiene-Styrene (ABS) plastic under hot air heating based on Computational Fluid Dynamics (CFD). Orthogonal experiments were conducted based on the parameter range of each factor obtained in the pre-experiment. The results showed that the outlet distance, hot air velocity, hot air temperature, and their interaction items are important factors that affect the melting rate of ABS materials. Based on the simulation results, a multiple nonlinear regression model was constructed to predict the melting rate under different parameters. Comparing the simulation values with the predicted values of the multiple nonlinear regression model, the error between the two was no >10%, indicating the effectiveness of the prediction model. The research results can help guide the hot air welding melting process for different materials and characteristics, and provide a certain reference value for the development of hot air welding technology.

Regression and Neural Network Analysis of the System of Cardiorespiratory Synchronism Parameters to Predict the Minimum Limit of the Synchronization Range

In order to improve the existing step-by-step principle of determining the limits of the range of cardiorespiratory synchronization (CRS) and the regulatory and adaptive capabilities of the human body, a regression analysis of measurements of the CRS parameters system was carried out. Statistical prediction of the minimum limit of the synchronization range in the CRS method was performed using linear, nonlinear multiple regression models, as well as neural network regression. The following parameters were used as the basis for predicting the minimum limit of the synchronization range: initial heart rate, initial respiratory rate, age and gender. The variable initial heart rate contributed the most to all regression models. A comparative analysis of the quality indicators of all models showed that the prediction of the minimum limit of the synchronization range can be carried out both by linear multiple regression and using neural network regression implemented by means of a 3-4-1 multilayer perceptrons. However, having already had a NN-regression forecast of maximum limit of the synchronization range, as well as a NN-classification of the level of regulatory-adaptive status, the use of NN-regression will be proposed as the main forecasting method for its implementation in the software of the CRS measurement system.

Comparison of Some Selected Models of Daily Water Supply in Abuja, Federa Capital Territory (FCT) Of Nigeria.

Water is considered as an essential commodity in human society. It has been observed that the basic standard of living of any Country can be measure through the effective portable water supply for consumption, sanitation and hygiene for the citizen. Its production and supply in the Federal Capital Territory (FCT), Abuja, Nigeria is a challenge, as dominant percentages of lower- class workforce live outside the FCTWB existing water network, and depend on borehole or tanker for water. The demand on the water supply system in FCT, Abuja has exceeded its initial design population, and the system is incomplete and operating with only 720 million liter of water per day (MLD) functioning plant. On this basis, this study seeks to identify the best model that fit daily water supply data in FCT, Abuja and to forecast the future daily water supply in this area. The comparing models were artificial neural, multiple linear and non-linear regression model were applied to the data set of daily water production, daily water treated, daily water supply, daily water leakage, Number of household connected to Federal Capital Territory Water board water network, Number of Commercial area connected to Federal Capital Territory Water board water network, and the Estimated population of Federal Capital Territory ,Abuja from (2013 to 2020), and it was emerged that the most adequate model for the data was Multiple linear regression model (MLR), with (R2 =1.000), (RMSE = 0.00282), and (MAE = 6.540). Artificial neural network records the high value of (RMSE = 432.4321), (MAE = 549.444) and (R2 =0.921), and multiple nonlinear regression record the highest value of (RMSE = 806.687), (MAE = 666.421) and (R2 = 0.999). From the statistical measure of goodness of fit, the three models compared results indicate that MLR model forecast are closely matching with actual observations values, artificial neural network model over-forecast some days volume of daily water supply by FCTWB, cooperation and multiple nonlinear regression model under-forecast some days volume of daily water supply by FCTWB, corporation. The three models compared depicts the influential variables of FCTWB Corporation daily water supply, (ANN = DWP, DWT), (MLR = DWT, DWL) and (MNLR = DWP, DWL), and their forecast graphs shows downward daily water supply in FCT, Abuja. It is recommended that the FCT, Abuja water Board Corporation should consider multiple linear regression (MLR) model and its forecasted values as a road map in its operations and planning toward water supply sustainability in FCT, Abuja.

Определение зависимости кажущейся плотности керамических образцов от формовочной влажности глинистого сырья и давления прессования на основе регрессионных моделей

The paper proposes to use white clay with the highest apparent density to improve the production of wire-ceramic resistors. Preliminary studies of white clay have shown that it is more sensitive to drying than red clay. The intensity of moisture output from the center of the product to its surface during the manufacturing process and the drying characteristics of white clay give greater shrinkage and strength of dry samples. Linear and nonlinear multiple regression models with and without multicollinearity are considered to find the dependence of the apparent density of clay raw materials on humidity and pressing pressure. To clarify the model structures, a two-stage approach is used, which involves building a model for residuals. It is found that the combination of a multiple regression model taking into account multicollinearity with an AR model constructed for residues makes it possible to calculate the apparent density of clay raw materials with the smallest standard deviation from experimental data. The obtained model structures will be used to determine the optimal parameters of forming ceramic samples: the moisture content of clay raw materials and the compaction pressure, at which the greatest apparent density of samples is achieved. This will improve the production technology of wire-ceramic resistors. The use of wire-ceramic resistors based on white clay with the highest apparent density will speed up one of the stages of the technological process – drying and reduce their hygroscopicity. As a result of the use of wire-ceramic resistors with the highest apparent density, the mechanical strength of the products will increase, which will improve the operational characteristics and prolong the life of the products.

Strength-based life cycle CO2 of fly ash concrete: Variation with common mix parameters

The addition of fly ash (FA) to concrete will not only reduce the CO2 content of concrete but also affect the compressive strength of concrete. However, most studies on carbon emissions have underestimated the complexity of the relationship of emissions with the concrete mix design and compressive strength. In this study, a database of mix designs was established by considering 1062 datasets related to the laboratory concrete mix designs and compressive strength values, and by using an artificial neural network model, a multiple nonlinear regression model, random forest model and support vector machines to simulate and predict the compressive strength of concrete. Moreover, the differences in the life-cycle CO2 corresponding to FA concrete among three different allocation types-no, mass, and economic value allocations-based on two functional units (FU)-per unit volume and per unit volume and strength were investigated. Finally, sustainable mix design method of FA concrete was established to find the optimal FAC mixtures and the correlation between CO2 emission and concrete mix parameters was analyzed by the maximal information correlation (MIC). The results indicate that the neural network model has a better regression effect than the multiple nonlinear regression model and other machine learning models on the prediction of compressive strength and CO2 emissions. For different functional units, the CO2 emissions under FU1 increases with the strength of concrete regardless of different allocation types; the CO2 emissions under FU2 varies considerably with changes in the amount of added FA under no allocation and economic value allocation. The CO2 emission under FU1 has a stronger correlation with various mix parameters than that under FU2.

Modeling and Optimization of Mechanical Cutting of Downhole Tubing

Summary Mechanical cutting of tubing plays a vital role in solving the problem of pipe string jams in workover operations of oil wells. To improve the efficiency of downhole cutting operations and save operation costs, it is necessary to optimize the parameters of downhole-cutting operations. However, previous research did not involve related engineering problems. Therefore, in this paper, the equivalent simulation experiment of downhole cutting is conducted based on actual field data. Cutting speed, feed rate, and cutting thickness are used as parameters while cutting power (P), material removal rate (MRR), and tool chip temperature (T) are used as optimization objectives with the trade-offs between the three objectives considered. The full factorial design is used to carry out the experiments and the combination of grey relational analysis (GRA) method and entropy weight method is used to determine the weight of the three objectives. The influence of cutting parameters on the optimization objectives is analyzed, the mathematical model between cutting parameters and a single objective is established, and the adaptive weight particle swarm algorithm is used to optimize the coefficients of this model. The relationship between the multiobjective model and cutting parameters is established using a multiple nonlinear regression model, and the selection of interaction terms is completed using a stepwise regression method. The reliability of the model is also verified. This paper provides a reference for future research on downhole-cutting problems.

Development of machine learning techniques in corrosion inhibition evaluation of 5-methyl-1 H-benzotriazole on N80 steel in acidic media

In this study, three machine learning models of data analysis based on multiple linear regression (MLR), multiple nonlinear regression (MNR), and artificial neural network (ANN) has been proposed to model the behavior of 5methyl-1 H-benzotriazole (m-BTA) inhibitor on N80 steel in acidic media at different temperatures and inhibitor concentration. Based on experimental observation, the DC trend removal by Polynomial detrending revealed a suitable trend between electrochemical noise (ECN) measurement data and the results obtained from electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization test at m-Factor equals to 3, 4, and 5. The inhibition efficiency (η) of 91% was achieved by using 0.001 M of m-BTA inhibitor. The corrosion rate will increase by increasing the temperature for the blank sample. Still, the m-BTA resulted in corrosion protection for N80 at high temperatures, and ECN resistance (Rn) increases with increased m-BTA concentration. The experimental tests revealed that temperature and m-factor are significant for corrosion inhibition of m-BTA; also, inhibitor concentration is an essential factor. Therefore, these three parameters were chosen as inputs for the Rn of the inhibitor. Removing unnecessary data from the original dataset causes relevant results compatible with experimental observations. The results of the reduced data set showed that R2 for the test of whole and reduced MLR model is 0.41 and 0.69, respectively, which shows that the reduced model is more suitable for modeling the training data than the whole model. The MNR model presents better accuracy than the MLR model, demonstrating that the relation between inputs and output does not follow a linear dependency. Several topologies of the ANN model were constructed, and the number of neurons was changed in one hidden layer. Finally, five neurons in the hidden layer with the Levenberg–Marquardt backpropagation (LMBP) algorithm were selected to develop the optimum ANN model, which has R2 and RMSE values of 0.99 and 17.41.

Індикатори змін рівня функціональних резервів організму операторів при розвитку вікових змін, втоми та асоційованих зі стресом захворювань

In the condition of war on the territory of our country, the intensity of the development of events that surround a person has changed significantly. In this regard, the need to apply simple and quick methods of assessing the functional state has increased. This especially applies to operators of unmanned aircraft systems, whose professional activity is associated with a large informational and physical load. The purpose of study was to study the possibilities of the stabilometry methods in assessing functional changes in the body of operators associated with the loss of their functional reserves. This affects the psychophysiological age, fatigue and deviations in operators of unmanned aircraft systems, which have a stress-associated diseases. The presence of unidirectionality of psychophysiological processes that occur with age changed, during the development of fatigue and diseases, caused by the influence of a stress factor is shown. A linear multiple regression model was built, which can be used to determine the level and rate of aging of operators, and a nonlinear multiple regression model that can estimate the degree of fatigue that develops during the year in the operator while performing official duties. With help of the method of discriminant analysis, special solving rules have been developed, according to which it is possible to determine whether the operator belongs to a group of healthy persons or those suffering from stress-associated diseases.

Experimental study on predicting head-cut migration rate of check dams

Head-cut erosion is the primary mechanism causing overtopping-induced breaches of check dams. Furthermore, the dam fill soil properties directly influence the head-cut migration rate. Although the fill materials of most flume model tests are cohesive soils, there are no experimental studies on loess materials with low erosion resistance. Accordingly, this study conducted 26 groups of flume model tests on the check dam head-cut migration mechanisms. Furthermore, it comprehensively analyzed the factors influencing the characteristics of head-cut migration in check dams, namely compactness, water content, inflow, and head-cut height, from the macro-and micro-viewpoints. The head-cut erosion rate models of compacted loess were developed considering multiple factors. A nonlinear multiple head-cut migration coefficient regression model was developed using the energy dissipation theory, the findings from the head-cut erosion tests, and experimental results from other studies, while considering the water content, compactness, clay content, inflow, and head-cut height as inputs. Seven sets of measured data with different erosion resistances were selected to verify the rationality and accuracy of the proposed model. In general, the sensitivity analysis results showed that the model could be used for cohesive soil with strong erosion resistance and silty sand and loess with weak erosion resistance. Its generalization and prediction accuracy have been further improved compared to other models of the same type.

Revisiting the hydrological basis of the Budyko framework with the principle of hydrologically similar groups

Abstract. The Budyko framework is a simple and effective tool for estimating the water balance of watersheds. Quantification of the watershed-characteristic-related parameter (Pw) is critical for accurate water balance simulations with the Budyko framework. However, there is no universal method for calculating Pw as the interactions between hydrologic, climatic, and watershed characteristic factors differ greatly across watersheds. To fill this research gap, this study introduced the principle of hydrologically similar groups into the Budyko framework for quantifying the Pw of watersheds in similar environments. We first classified the 366 selected watersheds worldwide into six hydrologically similar groups based on watershed attributes, including climate, soil, and vegetation. Results show that soil moisture (SM) and fractional vegetation cover (FVC) are two controlling factors of the Pw in each group. The SM exhibits a power-law relationship with the Pw values, with increasing SM leading to higher Pw values in dry watersheds (SM ≤ 20 mm) and lower Pw values in humid watersheds (SM > 20 mm). Additionally, the FVC shows to be linearly correlated with the Pw values in most hydrologically similar groups, except in that group with moist soil and no strong rainfall seasonality (SM >20 mm and seasonal index (SI) ≤ 0.4). Multiple non-linear regression models between Pw and the controlling factors (SM and FVC) were developed to individually estimate the Pw of six hydrologically similar groups. Cross-validations using the bootstrap sampling method (R2=0.63) and validations of time-series Global Runoff Data Centre (GRDC) data (R2=0.89) both indicate that the proposed models perform satisfactorily in estimating the Pw parameter in the Budyko framework. Overall, this study is a new attempt to quantify the unknown Pw in the Budyko framework using the method for hydrologically similar groups. The results will be helpful in improving the applicability of the Budyko framework for estimating the annual runoff of watersheds in diverse climates and with different characteristics.

Insights into the degradation of high-density polyethylene microplastics using microbial strains: Effect of process parameters, degradation kinetics and modeling

The extensive distribution of microplastics and their abundance around the world has raised a global concern because of the lack of proper disposal channels as well as poor knowledge of their implications on human health. Sustainable remediation techniques are required owing to the absence of proper disposal methods. The present study explores the deterioration process of high-density polyethylene (HDPE) microplastics using various microbes along with the kinetics and modeling of the process using multiple non-linear regression models. Ten different microbial strains were used for the degradation of microplastics for a period of 30 days. Effect of process parameters on the degradation process was studied with the selected five microbial strains that presented the best degradation results. The reproducibility and efficacy of the process were tested for an extended period of 90 days. Fourier-transform infrared spectroscopy (FTIR) and field emission-scanning electron microscopy (FE-SEM) were used for the analysis of microplastics. Polymer reduction and half-life were evaluated. Pseudomonas putida achieved the maximum degradation efficiency of 12.07% followed by Rhodococcus ruber (11.36%), Pseudomonas stutzeri (8.28%), Bacillus cereus (8.26%), and Brevibacillus borstelensis (8.02%) after 90 days. Out of 14 models tested, 5 were found capable of modeling the process kinetics and based on simplicity and statistical data, Modified Michaelis-Menten model (F8; R2 = 0.97) was selected as superior to others. This study successfully establishes the potential of bioremediation of microplastics as the viable process.

Modeling of pertinent parameters influence on the time dependent mass transfer coefficient of particulate matter under the sink effect

One of the most crucial evaluation metrics for the performance of particle sink purification technology is the time-dependent mass transfer coefficient (TDMTC). Therefore, it is very helpful for designers and developers to accurately describe the functional relationship between different influence parameters and the TDMTC. In this paper, four influence parameters (the applied voltage (V), interelectrode distance (dc), porosity (P), and shape (n) of the collecting electrode) were considered, and then non-linear multiple regression (NLMR) and multi-gene genetic programming (MGGP) methods were used to establish prediction models of the TDMTC. Results showed that V and n were the most significant factors, followed by dc and P. Both multi-factor models could accurately predict the TDMTC under the sink effect with a maximum prediction error of 20% and 15%, respectively. Moreover, for particulate matter (PM) with different size fractions, MGGP models could improve the prediction accuracy by 5–10% compared to NLMR models.

Aging affects isomer-specific occurrence of dechlorane plus in soil profiles: A case study in a geographically isolated landfill from the Tibetan Plateau

Two major structural isomers in commercial dechlorane plus (DP) mixtures, anti-DP and syn-DP, generally displayed varied desorption and partitioning efficiencies in soils, which may be linked to their different aging rates. However, the molecular parameters that govern the degree of aging and its associated effects on the occurrence of DP isomers have not been comprehensively investigated. In this study, the relative abundance of rapid desorption concentration (Rrapid) was measured for anti-DP, syn-DP, anti-Cl11-DP, anti-Cl10-DP, Dechlorane-604 (Dec-604), and Dechlorane-602 (Dec-602) at a geographically isolated landfill area in the Tibetan Plateau. The Rrapid values were used as an indicator of aging degree, exhibiting a close correlation with the three-dimension conformation of the molecules for the dechlorane series compounds. This observation suggested that planar molecules may have a greater tendency to accumulate in the condensed phase of organic matter and undergo more rapid aging. The fractional abundances and dechlorinated products of anti-DP were found to be predominantly controlled by the aging degree of DP isomers. The multiple nonlinear regression model indicated that differences in aging between anti-CP and syn-DP were primarily driven by the total desorption concentration and soil organic matter content. Aging plays a significant role in both the transport processes and metabolism of DP isomers and should be taken into account to refine the assessment of their environmental behaviors.

Use of non-linear multiple regression models for setting water quality criteria for copper: Consider the effects of salinity and dissolved organic carbon

Cu pollution is a global concern because of its high toxicity and persistence. Few investigations have been conducted on the effects of salinity and dissolved organic carbon (DOC) on Cu toxicity and water quality criteria (WQC). To analyze their impact on the WQC of Cu, non-linear multiple regression (NLMR) models based on salinity and DOC were constructed. The NLMR models demonstrated that when salinity increased, the toxicity values for Cu on fish, mollusca, rotifer, and echinodermata first increased and then declined, whereas those for arthropoda and algae increased. These findings demonstrate that salinity has a substantial impact on Cu toxicity, primarily owing to changes in physiological activity. The original and corrected WQC values in the upper, middle, and outer regions of the Yangtze Estuary were derived based on the species sensitivity distribution method. These values were 1.49, 3.49, 8.86, and 0.87 μg·L−1. An important finding was that lower Cu concentrations in the outer areas posed the highest ecological risk owing to the effects of salinity and DOC. NLMR models are applicable to other coastal areas worldwide. This provides valuable information for the establishment of an accurate and protective estuary for Cu-related WQC.

Just Suspended Speed Simulation in Torus Reactor Using Multiple Non-Linear Regression Model

In the chemical and water treatment industries, it is necessary to achieve maximum contact between the solid and liquid phase, thus promoting the mass and heat transfer, to obtain a homogeneous solution. Increasing stirring speed is the most recommended solution in different types of reactors: stirred tank, column, and tubular. However, this inadvertently increases the energy consumption of the industry. Determination of the minimum speed, labeled the just suspended speed (Njs) and crucial to attaining homogeneity, has been widely investigated. Numerous studies have been carried out to assess formulas for determining the solid particle speed in various reactor types. Given the limitations of the existing formulations based on a generalization of a unique equation for computing Njs for all soil classifications, it appears that most formulas can only approximate complex phenomena that depend on several parameters. A novel formula was developed, and the results given in this paper demonstrate the effectiveness of generating significant uncertainties for the estimation of Njs. The purpose of this study was the elaboration of experiment-based data-driven formulas to calculate Njs for different particle size classes. Nonlinear multiple regression (MNLR) models were used to generate the new formulas. The gradient descent optimization algorithm was employed to solve the hyperparameters of each novel equation, utilizing supervised learning. A comparison of the data indicated that the unique formulas presented in this study outperformed empirical formulas and provide a useful means for lowering energy consumption, while increasing the heat and mass transfer in torus type reactors.

Research on Water-Conducting Fractured Zone Height under the Condition of Large Mining Height in Yushen Mining Area, China

Abstract Accurately predicting the development height of the water-conducting fracture zone (HW) is imperative for safe mining in coal mines, in addition to the protection of water resources and the environment. At present, there are relatively few fine-scale zoning studies that specifically focus on predicting the HW under high-intensity mining conditions in western China. In view of this, this paper takes the Yushen mining area as an example, studies the relationship between the water-conducting fissure zone and coal seam mining height, coal seam mining depth, hard rock scale factor, and working face slope length, finally proposing a method to determine the development height of the HW based on multiple nonlinear regression models optimized using the entropy weight method (EWM-MNR). To compare the reliability of this model, random forest regression (RFR) and support vector machine regression (SVR) models were constructed for prediction. The findings of this study showed that the results of the EWM-MNR model were in better agreement with the measured values. Finally, the model was used to accurately predict the development height of the hydraulic conductivity fracture zone in the 112201 working face of the Xiaobaodang coal mine. The research results provide a theoretical reference for water damage control and mine ecological protection in the Yushen mine and other similar high-intensity mining areas.

Locally R-optimal designs for a class of nonlinear multiple regression models

This paper concerns with optimal designs for a wide class of nonlinear models with information driven by the linear predictor. The aim of this study is to generate an R-optimal design which minimizes the product of the main diagonal entries of the inverse of the Fisher information matrix at certain values of the parameters. An equivalence theorem for the locally R-optimal designs is provided in terms of the intensity function. Analytic solutions for the locally saturated R-optimal designs are derived for the models having linear predictors with and without intercept, respectively. The particle swarm optimization method has been employed to generate locally non-saturated R-optimal designs. Numerical examples are presented for illustration of the locally R-optimal designs for Poisson regression models and proportional hazards regression models.

Estimation of sea surface pCO2 and air–sea CO2 flux in the East China Sea using in-situ and satellite data over the period 2000–2016

The partial pressure of carbon dioxide (pCO2) is an important variable to characterize the state of the seawater carbonate system. It is required to calculate the air-sea CO2 flux (FCO2) and for understanding ocean acidification. Surface ocean pCO2 is influenced by thermodynamic process, physical mixing, biological activity and air-sea gas exchange. Knowledge of the surface pCO2 in the East China Sea (ECS) is limited due to a lack of observations and well-informed models. This study employed in situ hydrographic (sea surface temperature and salinity) and carbonate system observations combined with satellite observations of chlorophyll a to develop a regional multiple non-linear regression (MNR) model to estimate pCO2 for three different physical-biogeochemical domains of the ECS. Comparison of the pCO2 from in situ observations with the MNR model had a root mean square error (RMSE) of 45.19 μatm and coefficient of determination (R2) of 0.87 in the transitional domain, an RMSE of 11.59 μatm and R2 of 0.92 in the shelf-dominated domain and an RMSE of 3.73 μatm and R2 of 0.97 in the open water domain. The reliability of the MNR model was also validated using an independent dataset with a good accuracy (RMSE of 9.15 μatm for domain II and RMSE of 7.71 μatm for domain III). The MNR model was applied to estimate the surface pCO2 and FCO2 for ten cruises on the period of 2013–2018, and also used to predict monthly surface pCO2 by using Changjiang Biology Finite-Volume Coastal Ocean Model (FVCOM) outputs for the period of 2000–2016 on the ECS inner shelf, and analyzed the seasonal and interannual variability of surface pCO2 and FCO2. The seasonal dynamics of sea surface pCO2 were mainly attributed to temperature changes, biological activities and water mixing process over the seasons. The time series of surface pCO2 and FCO2 on the ECS inner shelf showed an increasing trend of 3.32 ± 0.79 μatm yr−1 and 0.018 ± 0.0125 mol CO2 m−2 yr−2, and the region was a CO2 sink with an CO2 uptake of 0.12 ± 0.48 Tg C yr−1.

Global evidence of environmental and lifestyle effects on medical expenditures across 154 countries

Increases of health care expenditures (HCEs) challenge the financial capacity of governments and bring into question the quality of health care services in each country. It is known that modifiable risk factors (e.g. alcohol consumption) and certain environmental variables allow HCEs to be modeled without impairing the quality of healthcare services. We provide a worldwide statistical analysis of how HCEs can be reduced and with what statistical power/probability. The design was retrospective and was based on linear and nonlinear multiple regression models. The HCEs, alcohol consumption, renewable energy consumption, suicide rate, economic reversal of the environmental damage caused by CO2 emissions (ERCDE) and sales-focused jobs (SJs) were measured. The type of government and the most searched Twitter worldwide topics were also analyzed. A total of 154 countries (n) participated. Reducing alcohol consumption, SJs and ERCDE predicts linear reductions of 33.1% of HCEs. Annual alcohol consumption between 4 and 5 L per person was found to have no negative impact on HCEs. Beyond this tipping point, alcohol consumption did predict significant increases in HCEs. It was also found that renewable energy consumption exponentially explained 35.2% of the reductions in HCEs. HCEs can be reduced in each country by controlling the consumption of renewable energies, the ERCDE, and the SJs. Specifically, by controlling alcohol consumption, SJs, and ERCDE the economic reduction in HCEs could be reduced annually by as much as $228.466 per person. We offer tipping points that governments can use to make effective health policy decisions that include sustainable development goals.