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Threshold effect of economic growth on domestic waste production: evidence from China

Li Yang Hong-Yan Wang Lan Yi Xiang-Zhen Shi Wei Deng
Archives of Environmental Protection | 2022 | vol. 48 | No 4 | 13-24 | DOI: 10.24425/aep.2022.143705
Keywords domestic waste per capita GDP source reduction panel threshold regression model
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Abstract

Since the implementation of the compulsory sorting of domestic waste policy in China, the participation rate of residents is low, which leads to the unsatisfactory result of terminal reduction of domestic waste. Therefore, the problem of domestic waste reduction still needs to rely on source reduction. Based on the panel data of 29 provincial capitals in China from 2009 to 2018, this study conducts a comprehensive threshold effect test on per capita GDP and other influencing factors of domestic waste production, conducts panel threshold regression for the factors with threshold value, and explores the nonlinear relationship between per capita GDP and domestic waste production under the influence of different threshold variables. The results show that when the urban population density is less than 272 people/km2, the increase of 1% of per capita GDP will lead to a decrease of 0.251% in the domestic waste production, otherwise, it will lead to an increase of 0.249%; when the per capita consumption expenditure is less than the threshold value of 10,260 yuan/year, the influence coefficient of per capita GDP is 0.155, which increases to 0.207 above the threshold. When the share of tertiary industry is taken as the threshold variable, the two threshold values are 61% and 71% respectively. Through the analysis of control variables, it has been found that population size and amount of courier per capita have significant positive effects on domestic waste production, while gas permeability and the number of non-governmental organizations have significant negative effects
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Authors and Affiliations

Li Yang
1
ORCID: ORCID
Hong-Yan Wang
1
Lan Yi
2
Xiang-Zhen Shi
1
Wei Deng
1
  1. International Business School, Shaanxi Normal University, China
  2. Jinhe Center for Economic Research, Xi’an Jiaotong University, China

Creep Behaviors at 275 °C for Aluminum-Matrix Nano-composite under Different Stress Levels

M. Azadi A. Behmanesh H. Aroo
Archives of Foundry Engineering | 2021 | vo. 21 | No 3 | 81-89 | DOI: 10.24425/afe.2021.138669
Keywords creep aluminum alloy Nano-composite nanoparticles regression model
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Abstract

Aluminum alloys, due to appropriate strength to weight ratio, are widely used in various industries, including automotive engines. This type of structures, due to high-temperature operations, are affected by the creep phenomenon; thus, the limited lifetime is expected for them. Therefore, in designing these types of parts, it is necessary to have sufficient information about the creep behavior and the material strength. One way to improve the properties is to add nanoparticles and fabricate a metal-based nano-composite. In the present research, failure mechanisms and creep properties of piston aluminum alloys were experimentally studied. In experiments, working conditions of combustion engine pistons were simulated. The material was composed of the aluminum matrix, which was reinforced by silicon oxide nanoparticles. The stir-casting method was used to produce the nano-composite by aluminum alloys and 1 wt.% of nanoparticles. The extraordinary model included the relationships between the stress and the temperature on the strain rate and the creep lifetime, as well as various theories such as the regression model. For this purpose, the creep test was performed on the standard sample at different stress levels and a specific temperature of 275 ℃. By plotting strain-time and strain rate-time curves, it was found that the creep lifetime decreased by increasing stress levels from 75 MPa to 125 MPa. Moreover, by comparing the creep test results of nanoparticle-reinforced alloys and nanoparticle-free alloys, 40% fall was observed in the reinforced material lifetime under 75 MPa. An increase in the strain rate was also seen under the mentioned stress. It is noteworthy that under 125 MPa, the creep lifetime and the strain rate of the reinforced alloy increased and decreased, respectively, compared to the piston alloy. Finally, by analyzing output data by the Minitab software, the sensitivity of the results to input parameters was investigated.
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Authors and Affiliations

M. Azadi
1
ORCID: ORCID
A. Behmanesh
1
H. Aroo
1
  1. Faculty of Mechanical Engineering, Semnan University, Iran

The Relationship Between Middle Income Trap and Structural Transformation: The Case of Selected Countries

Semanur Soyyiğit
Central European Journal of Economic Modelling and Econometrics | 2019 | No 4 | 217-235 | DOI: 10.24425/cejeme.2019.131538
Keywords middle-income trap structural transformation economic complexity index per capita GDP random coefficient panel regression model
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Abstract

Many studies on middle income trap draw attention to the product trapt hat can be expressed as the fact that countries are stuck in the production and export of unsophisticated products. In this sense, it is stated that the role of a country in the production and export of sophisticated goods is one of the determinant factors to increase the level of income. In the literature, the concept of economic complexity, which is expressed as gaining competitiveness of complex products in terms of production and export, is noteworthy in recentyears. In this framework, relationship between the per capita GDP and the economic complexity is examined with regression analysis in this study for selected countries with high-level of income. In the analysis, in which random coefficient panel regression model is applied, a significant relationship was found between the two variables for Austria, Finland, Hong Kong, Japan, Norway,Singapore and Sweden.

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Authors and Affiliations

Semanur Soyyiğit

Dynamic modelling of an anaerobic reactor treating coffee wet wastewater via multiple regression model

Yans Guardia-Puebla Edilberto Llanes-Cedeño Ana Velia Domínguez-León Quirino Arias-Cedeño Víctor Sánchez-Girón Gert Morscheck Bettina Eichler-Löbermann
Journal of Water and Land Development | 2021 | No 50 | 229-239 | DOI: 10.24425/jwld.2021.138178
Keywords coffee wet wastewater modelling multiple regression model upflow anaerobic sludge blanket (UASB)
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Abstract

A multiple regression model approach was developed to estimate buffering indices, as well as biogas and methane productions in an upflow anaerobic sludge blanket (UASB) reactor treating coffee wet wastewater. Five input variables measured (pH, alkalinity, outlet VFA concentration, and total and soluble COD removal) were selected to develop the best models to identify their importance on methanation. Optimal regression models were selected based on four statistical performance criteria, viz. Mallow’s Cp statistic (Cp), Akaike information criterion ( AIC), Hannan– Quinn criterion ( HQC), and Schwarz–Bayesian information criterion ( SBIC). The performance of the models selected were assessed through several descriptive statistics such as measure of goodness-of-fit test (coefficient of multiple determination, R2; adjusted coefficient of multiple determination, Adj-R2; standard error of estimation, SEE; and Durbin–Watson statistic, DWS), and statistics on the prediction errors (mean squared error, MSE; mean absolute error, MAE; mean absolute percentage error, MAPE; mean error, ME and mean percentage error, MPE). The estimated model reveals that buffering indices are strongly influenced by three variables (volatile fatty acids (VFA) concentration, soluble COD removal, and alkalinity); while, pH, VFA concentration and total COD removal were the most significant independent variables in biogas and methane production. The developed equation models obtained in this study, could be a powerful tool to predict the functionability and stability for the UASB system.
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Authors and Affiliations

Yans Guardia-Puebla
1
ORCID: ORCID
Edilberto Llanes-Cedeño
2
ORCID: ORCID
Ana Velia Domínguez-León
3
Quirino Arias-Cedeño
1
ORCID: ORCID
Víctor Sánchez-Girón
4
ORCID: ORCID
Gert Morscheck
5
Bettina Eichler-Löbermann
5
ORCID: ORCID
  1. University of Granma, Study Center for Applied Chemistry, Cuba
  2. Faculty of Architecture and Engineering, International SEK University, Quito, Ecuador
  3. Language Center, University of Granma, Cuba
  4. College of Agricultural, Food and Biosystems Engineering, Technical University of Madrid, Spain
  5. Faculty of Agronomy and Crop Science, University of Rostock, Germany

The identification method of the coal mill motor power model with the use of machine learning techniques

Zofia Magdalena Łabęda-Grudziak Mariusz Lipiński
Bulletin of the Polish Academy of Sciences Technical Sciences | 2021 | 69 | No. 1 | e135842 | DOI: 10.24425/bpasts.2021.135842
Keywords coal mill motor power nonlinear model identification machine learning additive regression model process monitoring
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Abstract

The article presents an identification method of the model of the ball-and-race coal mill motor power signal with the use of machine learning techniques. The stages of preparing training data for model parameters identification purposes are described, as well as these aimed at verifying the quality of the evaluated model. In order to meet the tasks of machine learning, additive regression model was applied. Identification of the additive model parameters was performed on the basis of iterative backfitting algorithm combined with nonparametric estimation techniques. The proposed models have predictive nature and are aimed at simulation of the motor power signal of a coal mill during its regular operation, startup and shutdown. A comparative analysis has been performed of the models structured differently in terms of identification quality and sensitivity to the existence of an exemplary disturbance in the form of overhangs in the coal bunker. Tests carried out on the basis of real measuring data registered in the Polish power unit with a capacity of 200 MW confirm the effectiveness of the method.
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Authors and Affiliations

Zofia Magdalena Łabęda-Grudziak
1
ORCID: ORCID
Mariusz Lipiński
2
  1. Warsaw University of Technology, Institute of Automatic Control and Robotics, ul. św. Andrzeja Boboli 8, 02-525 Warsaw, Poland
  2. Institute of Power Systems Automation, ul. Wystawowa 113, 51-618 Wrocław, Poland

Application of projection pursuit regression model for blasting vibration velocity peak prediction

Jianjun Shi Huaming An Xin Wei
Archives of Civil Engineering | 2021 | vol. 67 | No 2 | 653-673 | DOI: 10.24425/ace.2021.137190
Keywords blasting vibration vibration velocity prediction projection pursuit regression model shallow tunnel genetic algorithms
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Abstract

Based on Projection Pursuit Regression Theory (PPRT), a projection pursuit regression model has been established for forecasting the peak value of blasting vibration velocity. The model is then used to predict the peak value of blasting vibration velocity in a tunnel excavation blasting in Beijing. In order to train and test the model, 15 sets of measured samples from the tunnel project are used as the input data. It is found that predicting results by projection pursuit regression model on the basis of the input data is much more reasonable than that predicted by the traditional Sodaovsk algorithm and modified Sodaovsk formula. The results show that the average predicting error of the projection pursuit regression model is 6.36%, which is closer to the measured values. Thus, the projection pursuit prediction model is a practical and reasonable tool for forecasting the peak value of blasting vibration velocity.
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Authors and Affiliations

Jianjun Shi
1
Huaming An
2
ORCID: ORCID
Xin Wei
3
  1. Associate Professor PhD., Eng., Beijing Key Laboratory of Urban Underground Space Engineering, School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing, China
  2. Lecturer, PhD., Eng., Kunming University of Science and Technology, Faculty of Public Security and Emergency Management, 650093, Kunming, China
  3. Master Studnet., Eng., University of Science and Technology Beijing, School of Civil and Resource Engineering, 650093, 100083, Beijing, China

Stiffness modulus prediction against basic physical and mechanical characteristics of recycled base course with foamed bitumen and emulsified bitumen

Grzegorz Mazurek Przemysław Buczyński Marek Iwański
Archives of Civil Engineering | 2023 | vol. 69 | No 3 | 95-112 | DOI: 10.24425/ace.2023.146069
Keywords deep recycling technology foamed bitumen multiple regression modelling stiffness modulus blended binder trial section
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Abstract

The paper’s objective was to present the results of predicting the stiffness modulus of a recycled mix containing a blended road binder with foamed bitumen and emulsified bitumen. The Sm (acc. to IT-CY) indirect tensile test was used at temperatures of -10°C, 5°C, 13°C and 25°C. Prediction of the stiffness modulus accounted for the effect of temperature, the type of road binders, the sampling location and the type of technology selected. All effects, except temperature, were included in the model by entangling their effects through recycled base course physical and mechanical characteristics, such as indirect tensile strength, compressive strength, creep rate, air void content and moisture resistance. As a result, it was possible to determine a regression model based on multiple regression with a coefficient of determination R² = 0.78. Temperature and compressive strength were found to have the strongest effect on the variability of stiffness modulus. However, indirect tensile strength also significantly affected the Sm characteristic. In addition, FB-RCM (foamed bitumen) recycled mixtures proved to be more favourable than EB-RCM (emulsified bitumen) mixtures as they exhibited a lower deformation rate while retaining limited stiffness.

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Authors and Affiliations

Grzegorz Mazurek
1
ORCID: ORCID
Przemysław Buczyński
1
ORCID: ORCID
Marek Iwański
1
ORCID: ORCID
  1. Kielce University of Technology, Aleja Tysiaclecia Panstwa Polskiego 7, 25-314 Kielce, Poland

Biometric indicators of growth and development of Lolium perenne and Trifolium repens in terms of recultivation of soil contaminated with petroleum

Bogumiła Pawluśkiewicz Ilona Małuszyńska Marcin Małuszyński Piotr Dąbrowski Tomasz Gnatowski
Journal of Water and Land Development | 2022 | Special Issue | 70-79 | DOI: 10.24425/jwld.2022.143722
Keywords germination dynamic Gompertz regression model lawn plants polycyclic aromatic hydrocarbons (PAH) pot tests remediation seedlings parameters
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Abstract

The aim of the research was to determine the factors defining the growth and development of Lolium perenne and Trifolium repens upon petroleum contamination. The top layer of clay soil contaminated with petroleum products resulting from an oil pipeline failure was collected for the tests. The control was the same type of uncontaminated soil with the addition of, under laboratory conditions, tissue paper. The research was conducted in two stages. The first concerned the germination process and seedlings parameters (Petri dishes). The germination energy ( GE) and the germination capacity ( GC) of seeds were determined. The seedling’s development was also evaluated based on ‘WinRhizo PRO 2009’ software. Then, in the second stage, pot tests were carried out, where the growth and development of species in the first year after sowing were temporarily measured. The parameters studied were the number, height, green and dry masses of the plants.
A Gompertz regression model describing seed species germination and number species as time dependent dynamic was applied. The data were analysed statistically using variance analysis (ANOVA) and the PCA (principal component analysis) method. The results of our study indicated that admixture of petroleum into the soil does not seriously affect the development dynamics of Lolium perenne seedlings. The diesel oil contamination mostly affects the germination of the Trifolium repens by a statistically significant increase of the maximum value of germination and increasing the maximum growth rate.
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Authors and Affiliations

Bogumiła Pawluśkiewicz
1
ORCID: ORCID
Ilona Małuszyńska
2
ORCID: ORCID
Marcin Małuszyński
1
ORCID: ORCID
Piotr Dąbrowski
1
ORCID: ORCID
Tomasz Gnatowski
1
ORCID: ORCID
  1. Warsaw University of Life Sciences – SGGW, Institute of Environmental Engineering, ul. Nowoursynowska 159, 02-776 Warsaw, Poland
  2. Warsaw University of Life Sciences – SGGW, Water Center, Warsaw, Poland

Evaluation of small-scale farmland water conservancy construction mode in China

Belachew D. Hambebo Hui Li
Journal of Water and Land Development | 2022 | No 55 | 78-83 | DOI: 10.24425/jwld.2022.142307
Keywords simple linear regression model small-scale farmland water conservancy facilities small-scale reservoir small-scale irrigation water conservation
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Abstract

This study aims to evaluate the construction mode of small-scale farmland water conservancy using secondary data from the China statistical yearbook (2000–2019), which was simply and statistically computed. To put it briefly, the simple linear regression model was used to analyse the number of small-scale reservoirs and irrigated areas relative to their capacities and effectiveness. The results showed that the number of small-scale reservoirs increased by 122.2 units of their capacity and the number of small-scale irrigated areas increased by 6.8 units of their effectiveness. The present study introduces the simple linear regression model and accounts for how the number of the small-scale reservoirs and irrigated areas has increased (the total number of reservoirs was 83,260 in 2000 and 98,822 in 2018) relative to their capacity and effectiveness, respectively. Of course, the capacity of water harvesting and the effectiveness of irrigated areas have shown a linear increase over time. Between 2000 and 2019, the capacity increased from 3842 to 7117 for large-scale reservoirs, from 746 to 126 for medium-scale reservoirs, and from 594 to 710 for small-scale reservoirs and their ranges were 3.2, 380, and 116, respectively. Furthermore, the findings of this evaluation provide insights for making decisions on water conservancy interventions.
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Authors and Affiliations

Belachew D. Hambebo
1
ORCID: ORCID
Hui Li
ORCID: ORCID
  1. Hunan Agricultural University, College of Economics, 1 Nonda Rd, Furong District, Changsha, China

Estimation of standard duration maximum rainfall by using regression models

Cahit Yerdelen Ömer Levend Asikoglu Mohamed Abdelkader Ebru Eris
Journal of Water and Land Development | 2021 | No 50 | 281-288 | DOI: 10.24425/jwld.2021.138184
Keywords Eastern Black Sea Region Marmara Region regression model standard duration maximum rainfall temporal distribution of maximum daily rainfall
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Abstract

Gauging stations of meteorological networks generally record rainfall on a daily basis. However, sub-daily rainfall observations are required for modelling flood control structures, or urban drainage systems. In this respect, determination of temporal distribution of daily rainfall, and estimation of standard duration of rainfall are significant in hydrological studies. Although sub-daily rainfall gauges are present at meteorological networks, especially in the developing countries, their number is very low compared to the gauges that record daily rainfall.
This study aims at developing a method for estimating temporal distribution of maximum daily rainfall, and hence for generating maximum rainfall envelope curves. For this purpose, the standard duration of rainfall was examined. Among various regression methods, it was determined that the temporal distribution of 24-hour rainfall successfully fits the logarithmic model. The logarithmic model’s regression coefficients (named a and b) were then linked to the geographic and meteorological characteristics of the gauging stations. The developed model was applied to 47 stations located at two distinct geographical regions: the Marmara Sea Region and Eastern Black Sea Region, Turkey. Various statistical criteria were used to test the method's accuracy, and the proposed model provided successful results. For instance, the RMSE values of the regression coefficients a and b in Marmara Regions are 0.004 and 0.027. On the other hand, RMSE values are 0.007 and 0.02 for Eastern Black Sea Region.
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Authors and Affiliations

Cahit Yerdelen
1
ORCID: ORCID
Ömer Levend Asikoglu
1
ORCID: ORCID
Mohamed Abdelkader
1
ORCID: ORCID
Ebru Eris
1
ORCID: ORCID
  1. Ege University, Faculty of Engineering, 35100, Bornova – İZMİR, Turkey

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