Search

Empirical and probabilistic risk analysis methods can relatively accurately predict the seismic vulnerability of reinforced concrete (RC) structures. Using various intensity measures to estimate and forecast the seismic hazard of RC structures can contribute to the development of typical structural seismic resilience and vulnerability models. However, traditional empirical and analytical vulnerability studies rely more on field observation data and seismic risk algorithms and less on numerical simulation analysis for validation and optimization, resulting in limitations and fuzziness in the accuracy of the developed structural risk models. To explore the damage modes of RC frame structures under different intensities, this paper innovatively combines numerical model algorithms with empirical vulnerability methods to conduct empirical vulnerability and numerical simulation analyses on RC structures. Using probability statistics and nonlinear regression analysis methods, a prediction model for estimating the fragility of RC structures was proposed, and 858 RC structure damage samples from a typical city (Dujiangyan) during the Wenchuan earthquake in China on May 12, 2008, were used for model verification and comparative analysis. Using seismic response analysis theory, 901,530 acceleration records of the Wenchuan earthquake detected by eight actual seismic stations were selected, and nonlinear dynamic time history analysis was conducted. A four-story RC structural model was established using finite element software, and numerical simulation analysis was conducted on the model using 117,863 real earthquake acceleration data points obtained from actual monitoring stations during the Wenchuan earthquake. The acceleration time history curves and incremental dynamic analysis curves of the RC structure under different intensity measures were generated. By combining the moire algorithm and numerical simulation technology, damage stress clouds of steel bars and concrete under different intensity measures were generated, and the accuracy of the developed empirical vulnerability model was verified via numerical simulation results. [ABSTRACT FROM AUTHOR]

In order to improve the identification accuracy and shorten the analysis time of poor households in poverty alleviation, this paper studies a logistic regression analysis algorithm of targeted poverty alleviation based on mobile big data. Based on the theories related to big poverty alleviation data, Apriori algorithm is used to mine the basic information of households collected through mobile network based on Maslow's hierarchy of needs theory. A multi-dimensional item data of poverty detection is obtained by analyzing the frequent itemsets of association rules in poor areas, and the poverty characteristics of poor areas from different dimensions are analyzed. Taking the big data platform of targeted poverty alleviation in Jiangxi Province, China, as an example, the economic assistance data is selected and sent into the k-means algorithm to cluster by taking the village as the unit. Then, combined with the correlation of poverty characteristics, the abnormal phenomena in poverty alleviation are found, and the effectiveness of the targeted assistance to poverty alleviation target areas is analyzed. Based on nonlinear logistic regression, the identification model of poor households is built, and the Spark frame is used to extract, transform and read the characteristics of samples respectively. Finally, the poor households are identified with the logistic regression algorithm. Experimental results show that the average recognition accuracy of poor households reaches 92%, and the mining time of poverty feature analysis is only 18 s, which improves the efficiency of data analysis than current algorithms. [ABSTRACT FROM AUTHOR]

Bamboo has great economic values and is used extensively in many industries, and their natural distribution range was divided into 12 zones in China according to the temperature of their geographical distribution in previous works. Different bamboo species had significantly different abilities in low-temperature tolerance, which need to be considered carefully during ex-situ introduction. In this paper, we observed and evaluated the low-temperature damage of 19 bamboo species in winter, and measured the physiological changes of bamboo leaves. A total of 3060 leaf samples were obtained from 102 core collections in 34 bamboo species from the 5 regions of Chinese mainland for anatomical comparison, in order to screen out the key anatomical indicators related to their low-temperature tolerance and to establish a mathematical prediction model for bamboo introduction. The results showed that the low-temperature resistance of clustered bamboos was generally lower than that of the scattered bamboos. The decreased temperature led to the constant decrease of net photosynthetic rate and transpiration rate, but the increase of soluble sugar content in all bamboo species. There was no dormancy for all bamboo species in winter. The temperate bamboos showed lower photosynthesis as compared to tropical bamboos in winter. The leaf shape of bamboos was closely related to their distribution. A total of 13 leaf indicators were screened and more suitable to estimate the low-temperature tolerant abilities of bamboos and to predict their distribution. The MNLR (multiple nonlinear regression) mathematical model showed the highest fitting degree and the optimal prediction ability in the potential northernmost introduction range of bamboos. This study lay a foundation for bamboo introduction, and could also reduce the economic losses caused by the wrong introduction. [ABSTRACT FROM AUTHOR]

Ordovician limestone water on the floor is a serious threat to the safety of coal mine production in the Weibei coalfield, and prediction of the floor failure depth is the key for evaluating the mining conditions under pressure. This paper combines the hydrogeological conditions of the no. 5 coal seam and uses the FLAC3D program to determine the floor failure depth under two-factor (mining depth and width) and multifactor (mining depth, width, and height) conditions via numerical calculations and analysis. We obtain the fitting formula for the floor failure depth and analyze the influence of various factors on it. The results show that when the mining width does not exceed 200 m, the mining width has the greatest influence on the floor failure depth, followed by mining depth and mining height. In this paper, the working face floor failure depths measured values of 18 flat seams in China are taken as samples for comparison with the values calculated via empirical formulas and fitting formulas, and the maximum and minimum absolute errors and relative errors are analyzed. The nonlinear fitting regression formulas offered in this paper are found to provide strong predictive value, high accuracy, and a relatively small error range. The reliability and rationality of the models are further verified, thus providing a reference for future mining operations under safe aquifer water pressure conditions in the Weibei coalfield. [ABSTRACT FROM AUTHOR]

Green insurance (GI), as an innovative product integrating environmental protection and the financial sector, not only contributes to improving regional environmental quality (EQ) but also enhances corporate environmental risk management and awareness, driving the flourishing development of green finance and the environmental protection industry. Therefore, understanding the relationship between GI and EQ is crucial. This article delves into the mechanisms through which GI influences EQ, proposing a hypothesis that suggests an inverted "U" shape impact. Subsequently, based on panel data from 30 provinces in China spanning from 2000 to 2021, nonlinear regression models and threshold regression models were constructed to test the hypothesis. The research findings indicate: (1) Results from the fixed-effects regression model demonstrate that the impact of GI on China's EQ follows an inverted "U" shape. (2) Results from the threshold regression model also reveal an inverted "U" shape impact of GI on China's EQ, with a threshold value of 2.196. (3) Economic level and industrial structure exhibit significant inhibitory effects on EQ improvement. Technological level and environmental regulations demonstrate notable promotional effects. Population size shows no significant impact on EQ improvement. The study identifies a nonlinear effect of GI on EQ improvement, surpassing existing linear effect research, deepening the understanding of its impact on EQ, and contributing to the enhancement of regional EQ. [ABSTRACT FROM AUTHOR]

As an important public facility, the number, area, and scale of hospital buildings are growing rapidly. The efficiency of nurses' rounds to beds is an important indicator of the efficiency of nursing units in ward buildings. Ward buildings occupy a very important position in the overall energy consumption of hospital building complexes. The type and scale of nursing unit floorplans are some of the key factors affecting the energy consumption of ward buildings. In this paper, three typical floorplan layout types of hospital ward buildings in cold regions of China are selected. The relationships between rounding efficiency, building energy consumption, floorplan layout, and building size were quantified using Origin based on linear regression and non-linear regression. The study showed that at 60 beds, the efficiency of nurse rounds was 35.68% higher in the double-corridor layout compared to the single-corridor. At 44 beds, the difference in average bed energy consumption between the double-corridor type and the single-corridor type is the greatest, with a 9.02% saving in energy consumption. This result confirms that the layout and scale of the ward building has a significant impact on the efficiency of nursing unit rounds and building energy efficiency. [ABSTRACT FROM AUTHOR]

Intelligent compaction (IC) technology have been used for quality control and quality assurance (QC/QA) in subgrade construction. The effective regression correlations between Intelligent Compaction Measurement Values (ICMV) and In-situ Measurement Values (ISMV, including compaction degree and subgrade modulus ELWD) are the essential prerequisite of using IC technology for QC/QA. This paper presents the results from an experimental research study that was conducted from a practical subgrade project of China to explore the regression relationships between ICMV and ISMV. Three types of ICMV, including CMV, CCV and Evib, were collected along with the corresponding positions of the rollers. Two types of ISMV, containing compaction degree and ELWD, were measured by ring sampler method and light weight deflectometer (LWD) at specified test points, respectively. Based on these data, the influences of roller parameters and subgrade properties on the regression relationships of ICMV and ISMV were investigated. In addition, linear regression and 5 nonlinear regression algorithms were compared. The results suggest that ICMV reflect the stiffness of subgrade more than reflecting the density. In the regression of ICMV and ISMV, subgrade properties are more important than roller parameters while both of them should not be neglected. The influences of underlying stiffness and roller amplitude are linear while that of roller speed and moisture content are nonlinear. Nonlinear algorithms, especially the random forest, have the better performance compared to linear algorithm. Moreover, the combination of random forest and linear algorithm can further improve the accuracy of ISMV prediction. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geomechanics is the property of Journal of Geomechanics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Evaluating the seismic vulnerability of regional group structures is essential for effectively quantifying the seismic resilience and risk analysis of regional cities. To study the degree of damage caused by a typical earthquake to the overall group of buildings in a specific area, this paper takes the field observation data of the Wenchuan earthquake on May 12, 2008, in China as the research background and makes empirical vulnerability statistics and classification on all the inspection data of structural groups in Dujiangyan city. The seismic risk experience database (8621 building samples) based on the overall regional group buildings is developed. Using nonlinear regression, probabilistic risk prediction, and a cumulative damage conditional probability model, the empirical vulnerability matrices of six typical groups of buildings in different seismic intensity zones are established. Considering the effects of seismic design, age, purpose, number of floors, and plane shape features on the seismic resilience and vulnerability of regional group structures, a multifactor vulnerability innovation comparison model based on updating the quantitative scale of vulnerability level is developed. Ultimately, an innovative seismic vulnerability update index domain assessment model is proposed to quantify the damage modes of regional group structures. Zonal fragility prediction models of six typical regional structure groups are established. In the analysis results of the multidimensional empirical vulnerability model, it is found that with the increase in the macroseismic intensity grade, the sample number distribution shows a decreasing trend. Additionally, an essential finding is that the damage of the regional reinforced concrete structure is relatively light, indicating that it has a certain seismic resilience. The empirical vulnerability database is used within the proposed model to obtain a seismic vulnerability index. [ABSTRACT FROM AUTHOR]

In order to better evaluate the performance of the base isolated structure under the near-fault earthquakes, this paper takes into consideration an existing engineering case study in China as the prototype, and uses OpenSEES platform to establish the nonlinear finite element model of the base isolated structure. The nonlinear response of the isolated structure under the near-fault earthquake is analyzed. The incremental dynamic analysis (IDA) method is used to calculate the damage probability of the structure under the nearfault earthquake, and the fragility curve of the base isolated structure is established. The fragility equation is obtained by nonlinear regression, and the error of fragility equation is analyzed. The results show that the maximum value of the inter-story drift of the upper structure under the action of near-fault earthquake is significantly greater than that under the action of far-fault earthquake. With the increase of seismic intensity, the damage probability of base isolated structure increases nonlinearly, and the maximum response value of horizontal displacement of bearing and inter-story drift of superstructure increases generally. In addition, the exceeding probability of the fragility curve based on PSDA is greater than that based on EDP criterion. When the sample points of the two methods are the same, the exceeding probability points calculated based on PSDA can be regarded as accurate values. The fragility curve based on PSDA may overestimate the exceeding probability to some extent, and the overestimation may be enlarged with the increase of failure stage. [ABSTRACT FROM AUTHOR]

This article investigates whether, and how, CEO educational background affects Chinese corporate risk-taking. Using a sample of 4681 firm-year observations from 2012 to 2020, we find that CEO educational background is negatively associated with corporate risk-taking. The nonlinear quadratic regression shows a convex relationship, consistent with the finding that the effect is more profound for the subsample with relatively lower education levels. The negative relationship is stronger for the firms with higher leverage, with lower tangibility, and in non-manufacturing industries. We also address the endogeneity issue using a two-stage least squares regression. This paper may provide valuable insights for shareholders, helping them to hire the most suitable CEOs to achieve shareholders' objectives and increase the corporation's competitiveness in the market. [ABSTRACT FROM AUTHOR]

Gas reservoirs discovered in the southern margin of the Junggar Basin generally have high temperatures (up to 172.22 °C) and high pressures (up to 171.74 MPa). If using the PVT laboratory to get the gas compressibility factor, data from the laboratory are so little that it will not satisfy the demands of reservoir engineering calculations. There are many empirical correlations for calculating the Z-factor; however, these correlations give large errors at high gas reservoir pressures. The errors in estimating the Z-factor will lead to large errors in estimating all the other gas properties such as gas formation volume factor, gas compressibility, and gas in place. In this paper, a new accurate Z-factor correlation has been developed based on PVT data by correcting the high-pressure part of the most commonly used Dranchuk-Purvis-Robinson Correlation. Multivariate nonlinear regression is used to establish the independent variable function of pseudo-critical temperatures and pressures. By comparing it with the PVT data, the DPR correlation is continuously corrected to be suitable for ultra-deep gas reservoirs with HTHP. The new correlation can be used to determine the Z-factor at any pressure range, especially for high pressures, and the error is less than 1% compared to the PVT data. Then, based on the corrected Z-factor, the Cullender-Smith method is used to calculate the bottom hole pressure in the middle of the reservoir. Finally, the Z-factor under reservoir conditions of well H2 is predicted and the Z-factor chart at different temperatures is provided. [ABSTRACT FROM AUTHOR]

With the rapid development of air transportation, the complexity, importance, and severity of civil aviation safety have gradually become prominent. It is essential to use various data to analyze and predict the level of aviation safety. This paper used a combined prediction model based on Induced Ordered Weighted Averaging (IOWA) operator to forecast the civil aviation incident rate. We compiled and calculated civil aviation incident data and total flight hours from 2008 to 2019 in China and took the civil aviation incident rate (incident numbers per ten thousand flight hours) as the prediction object. First, this paper used the nonlinear regression model, Grey Verhulst model, and Holt-Winters exponential smoothing model to forecast the civil aviation incident rate individually. Then, it used the smallest sum of squared errors as the principle to use a combined prediction model based on the IOWA operator. It can be seen from the experimental results that the prediction accuracy of the combined model is better than single models. Finally, this paper forecasted the civil aviation incident rate in 2020 and 2021. The results showed that the predicted rates are 0.524 and 0.551. Most notably the incident rate will increase significantly compared with 2019. [ABSTRACT FROM AUTHOR]

Agricultural nonpoint source (NPS) pollution loss is closely related to hydrological processes. Understanding the differences in NPS pollution load loss under hydrological processes is useful for the management and prevention of NPS pollution. In this paper, hydrological and water quality data from 2016 to 2018 and monitoring data of physical and chemical indicators in 1347 field soil samples in the Shaying River Basin (SYRB) were used to analyze spatiotemporal variations in NPS pollution using the Soil and Water Assessment Tool and multifactor analysis of variance. The intensities and differences in NPS pollution losses for different soil types and land use patterns were evaluated under different hydrological zones. The annual rainfall in the SYRB decreased gradually from 1136.50 to 404.04 mm, showing a significant zoning. Areas with high loss intensities were mainly distributed in areas with steep slopes and in the 800–1000 mm rainfall zone. Cultivated land had the largest loss of NPS pollution, followed by forest land and rural residential land. Fluvo-aquic soil had the largest loss of NPS pollution, followed by cinnamon soil and lime concretion black soil. A nonlinear regression model was established for rainfall and the NPS pollution loss intensity and had a correlation coefficient of 0.60–0.99 at a 95% confidence level. Slope and rainfall were the main factors influencing the nitrogen and phosphorus losses. In the 800–1000 mm rainfall zone, the soil background nitrogen and phosphorus load was also a major factor influencing the nitrogen and phosphorus loss intensities. [ABSTRACT FROM AUTHOR]

In the north of China, during winter, a large number of activities (such as leisure, work, sightseeing and sports) are engaged in. This paper mainly focuses on human thermal perceptions of outdoor activities in a winter climate, especially the change in thermal perceptions when humans enter a temporary rest space with a shelter effect. A climate chamber was applied in our experiments, and temperature up-steps of 4, 8 and 12 °C were set, respectively. Twenty four college students were invited to engage in activities of different intensities, such as standing, walking (slowly) and biking. Through questionnaire survey and field measurement, the subjects' thermal sensation, thermal comfort and skin temperature were obtained. Hypothesis testing and non-linear regression methods were introduced to analyze experiment data. Major results were as follows. After temperature up-step changes, thermal sensation and skin temperature reach steady state within 30 min. However, the change in skin temperature caused by a short-term thermal experience does not disappear completely within 40 min. In addition to the influence of ambient temperature changes, activity intensity also influenced the variation in thermal sensation (subjective) and skin temperature (objective). These study results provide a scientific reference for future research and design of a temporary rest space in low ambient temperatures. Experimental studies including broader age groups and outdoor field tests are valuable for future research. [ABSTRACT FROM AUTHOR]

This paper attempts to investigate the impacts of inter-city high-speed rail (HSR) on tourism arrivals by employing a novel data of check-ins generated from social media. This type of check-in data collected in a case study (Hangzhou, China) reveals its high correlation with tourism activities and is feasible to act as a proxy of real tourism arrivals. A nonlinear regression model is developed to discover the temporal redistribution of tourism arrivals caused by HSR on weekends and holidays. Results show that Nanjing-Hangzhou HSR can significantly raise the number of visitors from Nanjing with a growth of 29.44% on Saturdays and 41.72% on Sundays. Further analysis on hourly distribution of these check-ins on weekends detects early arrival on Friday nights and longer stay on Sunday afternoons after HSR operates. Moreover, negative effect of seasonal climate change on tourism is also alleviated by HSR. This paper verifies the effectiveness of social media check-in data in tourism research and proposes practicable methodologies to quantitatively analyse this type of data. [ABSTRACT FROM AUTHOR]

Based on the panel data of 30 provinces (except for Tibet, Hong Kong, Macao, and Taiwan) in China from 2005 to 2016, a nonlinear threshold regression model and a carbon emission expansion models were constructed to empirically analyze the threshold effect of inward foreign direct investment (IFDI) and outward foreign direct investment (OFDI) on carbon dioxide emission intensity in China. The results show that (1) China's OFDI has increased carbon dioxide emission intensity while the IFDI has a significant inhibitory effect on carbon dioxide emission intensity. (2) The impact of the OFDI on carbon dioxide emission intensity gets influenced by the threshold effect of population size, economic development level, technology level, and environmental regulation. (3) The impact of the IFDI on carbon dioxide emission intensity also has threshold characteristics affected by population size, economic development level, and technological level. Hence, China should introduce more IFDI, optimize the structure of the OFDI, and exert its environmental improvement effect to satisfy the carbon emission reduction goal earlier. [ABSTRACT FROM AUTHOR]

Seismic intensity measures are essential quantitative parameters for evaluating and predicting engineering structures' seismic risk and vulnerability. Various regional earthquake analysis and vulnerability models with evaluation and prediction capabilities have been developed using macroseismic and instrumental intensity measures. These results have significant positive implications for evaluating large-scale regional earthquake resilience and predicting earthquake risks. However, the traditional determination of seismic intensity measures is based on given seismic station detection data and macroscopic intensity standards, and the developed seismic risk models are relatively outdated, resulting in relatively low accuracy in assessing the vulnerability of regional structures. This paper proposed an improved instrumental intensity calculation equation and updated the model using 216,000 acceleration records monitored by nine typical seismic stations in China during the Jiuzhaigou earthquake on August 8, 2017. The proposed calculation model was combined with the latest version of China's macrointensity standard, and undamaged building samples were sufficiently considered. The vulnerability level of 1,499 buildings surveyed in the Jiuzhaigou earthquake was assessed. This paper innovatively proposed a membership index calculation model to estimate the impact relationship between different vulnerability levels. A comparative curve model was generated with the updated vulnerability databases of four typical regional structures, considering different vulnerability membership indices. Using the improved seismic intensity measures and nonlinear regression model, an improved vulnerability fitting model was developed to assess the seismic risk of regional structures. The model was analysed and verified using field survey data from the Jiuzhaigou earthquake. • This work improves old seismic intensity measures and proposes an instrument intensity model. • An index calculation model of vulnerability membership is innovatively proposed. • The outdated seismic vulnerability regression model has been improved. • A relatively reasonable optimized Gaussian distribution model is proposed. [ABSTRACT FROM AUTHOR]

Tidal bore impact can be strong and destructive, placing estuarine infrastructures under great threat. However, there is a lack of research focusing on accurately estimating the impact pressure exerted by tidal bores. Herein new experiments were conducted to investigate the pressure of tidal bore fronts in a glass flume. Through analysis of instantaneous pressure of three forms of tidal bore, it was observed that the pressure fluctuation of weak and strong breaking bore fronts is characterized by impact pressure. The vertical distribution and maximum impact pressure of tidal bore were studied.The maximum impact pressure of breaking bore fronts appeared around 0.46 times height of it. The relationship between relative impact pressure and height of the tidal bore fronts was found to closely follow a normal probability density function. Through nonlinear regression analysis, an empirical equation was derived to calculate impact pressure, which was validated using observation data from the Qiantang River in China. This equation can be utilized to predict the impact pressure of tidal bore fronts and provide valuable support for estuarine engineering design. [ABSTRACT FROM AUTHOR]

Many developing countries including China are implementing increasingly stringent environmental regulations to achieve sustainable development. However, we have limited understanding about whether environmental regulations promote enterprise innovation. To address this research gap, this study empirically analyzes the impact of environmental regulations which is represented by the China Environmental Protection Law (2015) on enterprise innovation and it explores the moderating effects of official communication on environmental regulations and corporate innovation. The Super-Slacks-Based Measure (Super-SBM) model and Multiple Nonlinear Regression model are used to analyze 34,074 samples in China's A-share market during the 2015–2017 period. Our results show that, in general, a higher intensity of environmental regulations is more beneficial to incentivize enterprises to implement innovation. Meanwhile, there is an inverted U-type relationship between official communication and innovation of enterprises. Furthermore, at present, the average level of official communication in China is too high, which will weaken the role of environmental regulation in promoting enterprise innovation. Overall, this study can help us better understand the politics behind enterprise innovation in countries. [ABSTRACT FROM AUTHOR]

The current standard for evaluating road conditions worldwide relies primarily on the Pavement Condition Index (PCI) and the International Roughness Index (IRI). The IRI can be further calculated to obtain the Riding Quality Index (RQI). To assess pavement damage, various imaging equipment is commonly utilized, providing consistent results that align with actual road conditions. For roughness detection, the Laser Profilometer offers excellent results but may not be suitable for rural roads with poor conditions due to its high inspection cost and the need for a stable environmental setting. Therefore, there is a pressing need to develop cost-effective, rapid, and accurate roughness inspection methods for these roads, which constitute a significant portion of the road network. This study examined the relationship between PCI and RQI using nonlinear regression on 30,088 valid pavement inspection records from various regions in China (totaling 24,624.222 km). Our objective was to estimate RQI solely from PCI data, capitalizing on its broad coverage and superior accuracy. Additionally, we explored how PCI levels impact RQI decay rates. The models in this study were compared to several models published in previous studies at last. Our findings indicate that the model performs best for low-grade roads with low PCI scores, achieving over 90% accuracy for both cement concrete and asphalt concrete pavements. Furthermore, different levels of pavement damage have distinct effects on RQI decay rates, with the most significant impact observed when the pavement is severely damaged. The models in this study outperformed all the other available models in the literature. Consequently, under limited inspection conditions in rural areas, pavement damage inspection results can effectively predict riding quality or roughness, thereby reducing inspection costs. Overall, this study offers valuable insights but has limitations, including limited global generalizability and the model's applicability to high-grade roads. Future research is needed to address these issues and enhance practical applications. [ABSTRACT FROM AUTHOR]

The market integration of real estate investment trusts (REITs) in the US and four Asian markets as well as between their local stock and REIT markets are investigated in this paper. Using a number of modern econometric techniques on three integration indictors/proxies: timevarying conditional correlations, dynamic risk connectivity (variancecovariance) and cause and effect dependency of linear/nonlinear spillover and connectedness, we find that the five REIT markets show less integration than their corresponding stock markets. Moreover, the modelling of the portfolio risk spillover and connectedness (with covariance) shows a higher average level of market integration for the Asian REIT group. The REIT markets have experienced some significant shifts in their net total and net-pairwise directional risk connectivity. Additionally, investors and policymakers are reminded that any modelling of the cause and effect dependency of the REIT markets should be implemented with linear regression equations and a nonlinear value at risk system in risk spillover and connectedness (with covariance). Finally, significant contagious effects are identified across the REIT markets and stock and REIT portfolios during the global financial crisis and China stock market crash. [ABSTRACT FROM AUTHOR]

The traditional evaluation methods of earthquake disaster loss does not have a unified index to make a comprehensive assessment of the loss of earthquake disaster, which will cause some difficulties in the application. In order to comprehensively evaluate the loss of earthquake disaster and carry out multiple models comparison research, this paper presents multi-model methods for evaluating the loss of earthquake disaster based on lifeyears. Through the improvement of the lifeyears loss theory and the analysis of the value of the method parameters, the calculation scheme was determined. A comparative study was conducted on the lifeyears loss of earthquake in China grouped by years and regions. The relationship between the total lifeyears loss and its influence factors, such as the affected population, the epicentral intensity, the earthquake magnitude and recovery time was studied. The results showed that four influencing factors were all positively correlated with the total lifeyears loss. The affected population has the greatest correlation and the correlation of the other three factors is decreasing in turn. The linear regression model, the neural network quantile regression model and the nonlinear model of gene expression programming were established on the basis of the analysis of the influencing factors and the comparative analysis was carried out. Earthquake disaster loss was evaluated and predicted by using the evaluation model, which reduced data dependence and simplified the assessment process. [ABSTRACT FROM AUTHOR]

Onsite Earthquake Early Warning (EEW) applications require robust predictive models that can relate the early part of the recorded seismic waves to seismicity and/or ground shaking characteristics, the latter representing the variables used to make decisions about raising or not an alarm. The use of limited information to establish these models within a real-time estimation setting, typically corresponding to the first few seconds after the identification of the seismic wave arrival, introduces significant uncertainty (variability) in the predictions. To increase the number of historical observations available for the model calibration, data from multiple seismic stations within the same geographic region are utilized for the latter task. Unfortunately, this introduces additional sources of variability in the predictive model development, originating from local geographical effects and site conditions that affect differently the underlying physics at each station. This paper introduces a hierarchical Bayesian model updating framework to address this challenge. To better focus the research effort, the calibration of earthquake magnitude nonlinear regression models based on the seismic maximum predominant period is investigated, though underlying principles can be extended to other type of decision variables and intensity measures used in EEW applications. Within the hierarchical updating framework, the predictive models for each station are separately calibrated (first level), while sharing information across the stations by selecting common calibration hyper-parameters (second level). This facilitates a reduction of the variability of the estimates originating from regional wave propagation effects while simultaneously providing sufficient amount of data for calibrating the predictive models even for stations with scarce available records. To support computational efficiency in the hierarchical calibration, conjugate priors are utilized, while for accommodating nonlinear regression relationships samples from the posterior distribution, representing the model calibration, are obtained using Metropolis-within-Gibbs sampling. As illustrative case study, the application to the Sichuan region of Southwestern China is examined, with extensive comparisons performed for the accuracy and robustness of the resultant probabilistic predictive models. • Moment magnitude nonlinear models for onsite early warning applications are calibrated. • The predictive models for each station are separately calibrated. • Information sharing across the stations is established within a hierarchical Bayesian updating scheme. • This allows reduction of the variability of the estimates originating from regional wave propagation effects. • Computational efficient implementation relies on conjugate priors and Metropolis-within-Gibbs sampling. [ABSTRACT FROM AUTHOR]

The medium-term power load forecasting is often disturbed by a variety of external factors (such as temperature, holidays and wind power) and uncertainties. Also, the factors affecting the medium-term power load forecasting are complex and changeable, and it is difficult to predict accurately. In the big data environment, how to obtain valuable information quickly in a variety of large number of influence factors has become the key to the power load forecasting problems. A method of density forecasting based on LASSO quantile regression was proposed in this paper. First, the important influence factors were selected from the various external factors affecting the power load forecasting, and the LASSO quantile regression model was established. Then, by using the triangular kernel function, LASSO quantile regression was combined with the method of kernel density estimation for the medium-term power load probability density forecasting. Taking the historical load and external influence factors (including temperature, holidays and wind power) of a sub-provincial city in eastern China as an example, the probability density prediction of medium-term power load was carried out. The average absolute error obtained was respectively 3.53% and 3.69% in the median and the mode, which was better than the results without considering the external factors and without variable selection. In order to further verify the superiority of the method, the method was compared with the nonlinear quantile regression (NLQR) and the quantile regression neural network based on triangle kernel (QRNNT) probability density forecasting methods. The results illustrate that this method can better solve the high-dimensional data problem in power load forecasting, and obtain more accurate results of power load forecasting. [ABSTRACT FROM AUTHOR]

This paper uses the median-of-means (MOM) method to estimate the parameters of the nonlinear regression models and proves the consistency and asymptotic normality of the MOM estimator. Especially when there are outliers, the MOM estimator is more robust than nonlinear least squares (NLS) estimator and empirical likelihood (EL) estimator. On this basis, we propose hypothesis testing Statistics for the parameters of the nonlinear regression models using empirical likelihood method, and the simulation performance shows the superiority of MOM estimator. We apply the MOM method to analyze the top 50 data of GDP of China in 2019. The result shows that MOM method is more feasible than NLS estimator and EL estimator. [ABSTRACT FROM AUTHOR]

This paper aims to monitor the ambient level of particulate matter less than 2.5 μ m (PM 2.5 ) by learning from multi-weather sensors. Over the past decade, China has established a high-density network of automatic weather stations. In contrast, the number of PM monitors is much smaller than the number of weather stations. Since the haze process is closely related to the variation of meteorological parameters, it is possible and promising to calculate the concentration of PM 2.5 by studying the data from weather sensors. Here, we use three machine learning methods, namely multivariate linear regression, multivariate nonlinear regression, and neural network, in order to monitor PM 2.5 by exploring the data of multi-weather sensors. The results show that the multivariate linear regression method has the root mean square error (RMSE) of 24.6756 μ g/m 3 with a correlation coefficient of 0.6281, by referring to the ground truth of PM 2.5 time series data; and the multivariate nonlinear regression method has the RMSE of 24.9191 μ g/m 3 with a correlation coefficient of 0.6184, while the neural network based method has the best performance, of which the RMSE of PM 2.5 estimates is 15.6391 μ g/m 3 with the correlation coefficient of 0.8701. [ABSTRACT FROM AUTHOR]

Industrial agglomeration (IA), a common industrial phenomenon, has been verified to have a significant impact on total factor productivity (TFP) in many industries. However, the impact of IA on TFP is seldom investigated in the construction industry, despite the existence of the industrial agglomeration phenomenon in the construction industry. As such, this study aims to probe into the impact of IA on TFP in the construction industry, so as to provide new insights into the industry development and improvement of TFP in the construction industry. Based on the competing results of the agglomeration effect and congestion effect caused by IA, this study proposed three hypotheses on the impact mechanism of IA on TFP in the construction industry. Then, the non-linear regression model and linear regression model were developed to test the hypotheses based on the provincial panel data from 2002 to 2017 in China. The empirical results reveal that IA has a positive linear impact on TFP in the construction industry, and the impact of IA on TFP in the Chinese construction industry during the observed period is in the embryonic stage. Besides, both the firm scale and economic development level have positive impacts on TFP, whereas the specialization structure has a negative impact. Hence, the government can encourage industrial agglomeration in the construction industry to enhance TFP, in order to leverage the knowledge spillovers, labor pool, and other benefits from IA. [ABSTRACT FROM AUTHOR]

Judging and predicting tree suitability is of great significance in the cultivation and management of forests. Background and Objectives: Due to the diversity of tree species for afforestation in China and the lack of experts or the limitations of expert knowledge, the site rules of tree species in some regions are lacking or incomplete, so that a small number of tree suitability empirical site rules are difficult to adapt to the afforestation expert system's diverse needs. Research Highlights: This paper explores an intelligent method to automatically extract rules for selecting favorable site conditions (tree suitability site rules) from a large amount of data to solve the problem of knowledge acquisition, updating and maintenance of suitable forest site rules in the expert system. Materials and Methods: Based on the method of site quality evaluation and the theory of the decision tree in knowledge discovery and machine learning, the dominant species of Chinese fir and Masson pine in the forest resources subcompartment data (FRSD) of Jinping County, Guizhou Province were taken as examples to select the important site factors affecting the forest quality and based on the site quality of potential productivity. Assessment methodology was proposed to determine the afforestation of a stand site by nonlinear quantile regression, the decision tree was constructed from the ID3, C5.0 and CART algorithms. Results: Finally, the best-performing CART algorithm was selected to construct the model, and the extractor of the afforestation rules was constructed. After validating the rules for selecting favorable site conditions of Chinese fir and Masson pine, the production representation method was used to construct the relationship model of the knowledge base. Conclusions: Intelligent extraction of suitable tree rules for afforestation design in an expert system was realized, which provided the theoretical basis and technical support for afforestation land planning and design. [ABSTRACT FROM AUTHOR]

The water-conducting fractured zone induced by mining under aquifers is channels for water and sand inrushes. A method based on multiple regression analysis and a geographic information system (GIS) is proposed to predict the height of the water-conducting fractured zone in this paper. Five main indicators are found to control the height of the water-conducting fractured zone during fully mechanized caving mining under aquifers in which the thickness of the coal seam is more than 3 m: thickness of the coal seam, proportion of hard rock, length of the panel, mined depth and dip angle. The height of water-conducting fractured zone and the predictive variables from eighteen coal mines in China are investigated. Based on information entropy theory, a nonlinear multiple regression model and the weight of the indicators are considered, and the nonlinear multiple regression model is used in the GIS. Then, the approach is validated with a case study of the Xiegou Coalmine in the Shanxi province of China, in which the thickness of the coal seam which is under aquifers is more than 3 m. Error analysis of the approach is calculated with different hydrogeology classification. The results indicate that this model is a useful tool for predicting and analyzing the height of the water-conducting fractured zone, since the height of the water-conducting fractured zone can be quantitatively calculated and visualized. [ABSTRACT FROM AUTHOR]

To deeply explore the seismic vulnerability characteristics of typical structures and obtain the differences in the seismic capacity of multiple structural categories in actual earthquakes, combined with mathematical statistics and probabilistic damage model analysis methods, the bulk of collection and statistics were made on the structural seismic damage observation data (98,051.8122 × 104 m2 and 995,269 buildings) of 213 destructive earthquakes in China from 1975 to 2013. Seismic damage sample databases of wooden roof truss structures, adobe and timber structures, brick wood structures, masonry structures, RC structures, and bottom frame seismic wall masonry structure were established. A seismic damage investigation and analysis were conducted. All samples' vulnerability grades were evaluated using the latest version of the Chinese seismic intensity scale (CSIS-20). The actual damage vulnerability probability matrix and surface model of structures in multiple intensity regions based on the investigated area and quantity parameters were established. A nonlinear regression prediction model analysis method was proposed. A typical structural vulnerability prediction model considering the failure ratio and exceedance probability in the multi-intensity region was established and verified by the earthquake damage database. In addition, a vulnerability matrix prediction model considering updating the mean vulnerability index parameter was proposed, and a comparison model of the vulnerability prediction matrix of typical regional structures was developed. [ABSTRACT FROM AUTHOR]

Scandium metal has generated a lot of interest during the past years. This is due to the various crucial applications it has found ground in and the lack of production in countries outside China and Russia. Apart from rare earth ores, scandium is present in a variety of wastes and by-products originating from metallurgical processes and is not currently being sufficiently valorised. One of these processes is the production of titanium dioxide, which leaves an acidic iron chloride solution with a considerably high concentration of scandium (10–140 ppm) and is currently sold as a by-product. This research aims to recover scandium without affecting the solution greatly so that it can still be resold as a by-product after the treatment. To achieve this, two commercial ion-exchange resins, VP OC 1026 and TP 260, are used in the column setup. Their breakthrough curves are plotted with mathematical modelling and compared. Results indicate that VP OC 1026 resin is the most promising for Sc extraction with a column capacity of 1.46 mg/mL, but Zr, Ti, and V coextract have high capacities, while Fe does not interfere with the adsorption. [ABSTRACT FROM AUTHOR]

Because of the relatively low accuracy of the current asphalt pavement performance prediction, a new pavement performance prediction model was established based on the particle swarm optimization (PSO) algorithm and support vector machine regression (SVR) algorithm. First, the SVR algorithm was introduced into the model to deal with the nonlinear regression. Then the PSO algorithm was applied to improve the searching efficiency and parameter continuity of the SVR algorithm. The pavement inspection data of an expressway in eastern China from 2006 to 2015 were used to verify the results, proving the feasibility of the PSO-SVR prediction model. The research results show that the model using particle swarm optimization has a fast convergence speed, and the optimized support vector machine has better rutting prediction performance and perfect generalization, and the prediction accuracy and reliability are higher than those of unoptimized support vector machine model. [ABSTRACT FROM AUTHOR]

This research describes the industries' development and efficiency distribution, investigates the process of industry efficiency influence of these industries' agglomeration effect on total factor productivity (TFP) change based China's science and technology service industries from 2010 to 2019 data. We analyse the main factors influencing TFP change and the stage effect difference of significant factors through using linear and non-linear regression equations by construct the panel regression equations. The empirical results show:1) the overall trend of science and technology service industry agglomeration in China is declining; 2) Provinces and regions exhibits a trend of respective agglomeration within the economic belt; 3) the different location entropies of the five regions lead to obvious differences in the development stages; 4) the influences of technology progress and foreign trade on TFP efficiency changes are generally significant; 5)Science and technology service industry have improved industrial efficiency in the central and western regions and made it easier for these regions to break through bottlenecks. We conclude that industrial efficiency improvements should be based on technology upgrading and expansion in infrastructure allocation, also offering more realistic solutions to promote system construction and interregional policy coordination. [ABSTRACT FROM AUTHOR]

The spatial decomposition of demographic data at a fine resolution is a classic and crucial problem in the field of geographical information science. The main objective of this study was to compare twelve well-known machine learning regression algorithms for the spatial decomposition of demographic data with multisource geospatial data. Grid search and cross-validation methods were used to ensure that the optimal model parameters were obtained. The results showed that all the global regression algorithms used in the study exhibited acceptable results, besides the ordinary least squares (OLS) algorithm. In addition, the regularization method and the subsetting method were both useful for alleviating overfitting in the OLS model, and the former was better than the latter. The more competitive performance of the nonlinear regression algorithms than the linear regression algorithms implies that the relationship between population density and influence factors is likely to be non-linear. Among the global regression algorithms used in the study, the best results were achieved by the k-nearest neighbors (KNN) regression algorithm. In addition, it was found that multi-sources geospatial data can improve the accuracy of spatial decomposition results significantly, and thus the proposed method in our study can be applied to the study of spatial decomposition in other areas. [ABSTRACT FROM AUTHOR]

In this study, an innovative MODIS fractional snow cover (SCF) data assimilation (DA) prototype framework that invokes machine learning (ML) techniques and Common land model (CoLM) is proposed to improve the estimation of the snow depth (SD) and the SCF. To validate our new framework, we analyzed two snow seasons from 2013 to 2015 at 46 stations in Northern Xinjiang in China. We developed 12 SCF DA schemes that represent different DA methods (direct insertion (DI) and Ensemble Kalman Filter (EnKF)), observational data (original data and gap‐filled MODIS SCF data), and observation operators (five new snow depletion curves (SDCs) defined using traditional multivariate nonlinear regression and four ML methods). While improving the frequency of the SCF observations in the DI‐based DA scheme only resulted in a marginal improvement in the snow estimates, by adding new SDCs fitted by ML techniques (e.g., deep belief network), and the gap‐filled MODIS SCF data to the EnKF‐based DA scheme, we were able to reduce model structural uncertainties of CoLM and achieve marked improvement in the accuracy of the snow estimates (RMSE = 5.92 cm, mean bias error = −1.94 cm, and average degree of improvement = 32.18% for SD estimates and RMSE = 15. 79%, mean bias error = −1.21%, and average degree of improvement = 47.95% for SCF estimates). Our results demonstrate the feasibility of improving snow estimates by combining the ML techniques with physically based snowpack model in a SCF DA framework. Key Points: An innovative MODIS fractional snow cover assimilation framework invokes machine learning techniques and Common Land Model is proposedFive new snow depletion curves (SDCs) are defined by using multiple nonlinear regression and four machine learning techniquesNew SDCs functions as both the improved parameterization scheme of Common Land Model and the observation operator of assimilation frame [ABSTRACT FROM AUTHOR]

As a result of the booming rural reconstruction in China, the recurring problem of "village sameness" reflects the inadequate understanding of inherent spatial characteristics of traditional settlements. To better understand the internal logic driving site selection of historic rural settlements, this study proposed a machine learning method based on the Gaussian mixture model (GMM). First, significant spatial variables controlling settlement distribution were deduced using contextual analysis, then a univariate GMM was implemented to examine the settlement distribution sensitivity for every variable. Finally, a multivariate GMM was utilized to perform a multivariate regression analysis on the nonlinear nonmonotonic relationship between significant control variables and land usage development potential, which was a simulation of people's optimizing selection of living space. In accordance with the abstracted spatial rules, the model was also used for predicting the spatial trends that could support regional planning activities. In additional, a comparison between the GMM and the logistic regression model was made using spatial feature recognition and the spatial characteristic regression. The results showed the comparative advantage of the GMM for its nonlinear nonmonotonic behavior. [ABSTRACT FROM AUTHOR]

To research the seismic damage characteristics, mechanism and vulnerability of multistorey reinforced concrete (RC) frame structures, statistics and analysis, were made on 930 RC frame structures in Dujiangyan during Wenchuan earthquake, China. Firstly, seismic damage of RC frame structure in Dujiangyan is investigated comprehensively. According to the investigation results, easily damaged locations of this kind of structural system are: infilled wall, frame column, beam-column, joints and stairs. However, a large number of RC frame structures are basically intact or slightly damaged. By using the method of numerical statistical analysis, the non-linear relationship model and the fitting curve of seismic damage investigation samples under multiple seismic damage grades are given. Considering the number of stories, multiple ages and seismic fortification influencing factors, the empirical seismic damage situation of structures under each factor is analyzed, and the non-linear regression curve is developed. The empirical seismic vulnerability matrix and continuous regression function model and curve of RC frame structure in multi-intensity region are established. A calculation model of mean seismic damage index (MSDI) is proposed, and the vulnerability matrix and regression curve based on this parameter are given in combination with the empirical seismic damage investigation data. The above research results can provide a basic reference for vulnerability analysis and intensity scale revision of RC structures. [ABSTRACT FROM AUTHOR]

Utilizing routine hourly meteorological data of Xinjin Airport and daily average PM2.5 concentration data for Chengdu, winter visibility characteristics at Xinjin Airport between 2013 and 2017 and their relationship with meteorological conditions and particulate matter were analyzed. Between 2013 and 2017, the average winter visibility in Xinjin Airport was lowest in January, followed by that in December. The occurrence frequency of haze days in winter was much higher than that of nonhaze (clean) days, being 90.2% and 9.8%, respectively. These were mainly mild haze days, with an occurrence frequency of 44.4%, while severe haze days occurred the least, with a frequency of 7.7%. The linear and nonlinear relationships between winter visibility, meteorological factors, and PM2.5 were measured using daily data in winter from 2013 to 2016. The linear correlation between PM2.5 concentration and visibility was the most evident, followed by that of relative humidity. Visibility had a higher nonlinear correlation with PM2.5 concentration, relative humidity, and dew point depression. When relative humidity was between 70% and 80%, the negative correlation between visibility and PM2.5 concentration was the most significant and could be described by a power function. The multivariate linear regression equation of PM2.5 concentration and relative humidity could account for 65.9% of the variation in winter visibility, and the multivariate nonlinear regression equation of PM2.5 concentration, relative humidity, and wind speed could account for 68.1% of the variation in winter visibility. These two equations reasonably represented the variation in winter visibility in 2017. [ABSTRACT FROM AUTHOR]

Existing runway residual life prediction models need to be modified using the historical data of evaluated airports during evaluation and management for civil airports in China. If the data measured in the field and the historical data used for model calibration do not represent the actual historical performance of the evaluated airport, the predicted performance of the revised model might be poor. This study used measured pavement performance data for local civil airports in Henan Province from 2007 to 2017. The joint-estimation method was used to establish a functional residual life prediction model for local civil airport pavement with another dataset. A functional residual life prediction model for airport pavement was proposed in consideration of the influence of aircraft traffic and the thickness of the pavement surface layer. Taking into account the differences between samples in the two datasets, nonlinear regression with random-effect analysis and joint estimation were used to explain unobserved heterogeneity at the sample level and heteroscedasticity in the dataset. Based on the results of the established residual life prediction model, the marginal effect of the model parameters and the prediction performance of the entire model were analyzed with the measured data from the local airport pavement. Finally, the engineering applicability of the calibrated prediction model for pavement residual life was further evaluated. [ABSTRACT FROM AUTHOR]

This study reviewed published biomass equations for Larix species in the literature and developed additive biomass equations (by nonlinear seemingly unrelated regression known as NSUR) and independent equations, respectively, based on DBH-only and DBH-H variables with the biomass data of 150 trees sampled in northeastern China. The applicability of fitted and published equations was comprehensively assessed with respect to Larix species, their provinces and origins when applied in northeastern China. The results showed that equations derived from the local study area did not always perform better than the published biomass equations in the literature. Tree-level equations performed slightly broader than stand-level equations. Nonlinear regression could provide higher applicability due to correction factors in the linear regression. Adding tree height as the second variable improved the applicability of stem and aboveground published equations. The applicability of original published equations was better for northeastern China for sites of similar latitude. When using published biomass equations, care should be taken to assess the uncertainty and applicability of biomass equations, which is important for the sustainable management of forested resources. Published information on biomass equations is especially necessary for forestry organizations that do not have the opportunity to develop their own equations. [ABSTRACT FROM AUTHOR]

The study analysed the effect of shelterwood-cut strips on the outermost crown profiles and crown characteristics of naturally regenerated Pinus tabuliformis saplings in northeastern China. A total of 49 regenerated saplings from shelterwood-cut strips and 30 from uncut strips were collected. Nonlinear quantile regression was used to develop the outermost crown profile model for the saplings from the shelterwood-cut and uncut strips. The quantile value suitable for describing the outermost crown profiles of the two types of strips was selected using nonparametric boundary regression. The difference in crown morphologies between the shelterwood-cut strips and uncut strips was compared. The results showed that with the same diameter at breast height, the crown radii of the uncut strip saplings were larger than those of the shelterwood-cut strip saplings within the range of 0.2--1.0 for the relative depth into the crown. The largest crown radius of the saplings from the uncut strips was larger than that of the saplings from the shelterwood-cut strips. The inflection points of the shelterwood-cut strip sapling crowns were larger than those of the uncut strip sapling crowns. The crown volume of the small uncut strip saplings was larger than that of the shelterwood-cut strip saplings, and the difference in crown volume decreased with increasing sapling size. The saplings in the early stage of the uncut strips showed a greater growth rate than those of the shelterwood-cut strips, but their growth rate slowed over the long term according to branch-length annual growth. The present study provides a reference for forest management strategy decision making in promoting natural regeneration. [ABSTRACT FROM AUTHOR]

Background. Due to the high maintenance costs, it is critical to make full use of operating rooms (ORs). Operative duration is an important factor that guides research on surgery scheduling. Clinical effects, for example, surgery type, rationally influences operative duration. In this study, we also investigate whether the planning and scheduling decisions in ORs influence the operative duration. Methods. For our study, we collected and reviewed data on 2,451 thoracic operations from a large hospital in China. The study was conducted over a period of 34 months. Linear and nonlinear regression models were used to detect the effects on the duration of the operations. We have also examined interactions between the factors. Results. Operative duration decreased with the number of operations a surgeon performed in a day (P < 0.001). It was also found that operative duration decreased with the number of operations allocated to an OR, as long as there were not more than four surgeries per day (P < 0.001). However, they increased with the number of operations if it was more than four (P < 0.01). The duration of surgery was affected by its position in the sequencing of surgeries performed by a surgeon. In addition, the effects of surgeons depend on the surgery type as well as the position in the sequencing order. Conclusions. Operative duration was affected not only due to clinical effects but also some nonclinical effects. Scheduling decisions significantly influenced operative duration. [ABSTRACT FROM AUTHOR]

Based on the traffic accidents statistical data of 10 typical freeways in mainland China, by using of some kinds of regression model, the influences of the average vehicle speed and the speed standard deviation on the traffic safety are studied. According to the regression results, the accidents show an increasing trend with the increase of the vehicle average speed and the speed standard deviation. On this basis, in view of the regression results, the strategy is put forward for controlling the vehicle average speed and the speed standard deviation, which has important theoretical and practical significance for improving highway safety. After a comprehensive comparison among these regression methods, it is found that the nonlinear regression method of user-defined model expression has the best fitting effect, and it can also more accurately describe the objective reality. It has high practicality and popularized value. [ABSTRACT FROM AUTHOR]

Visibility degradation is a pervasive environmental problem in winter in China and its prediction accuracy is therefore important, especially in low visibility conditions. However, current visibility parameterization algorithms tend to overestimate low visibility (<5 km) during haze–fog events. The key point of low visibility calculation and prediction depends on a reasonable understanding of the correlation between visibility, PM2.5 concentration, and relative humidity (RH). Using the observations of PM2.5 concentration and meteorology from December 2016 to February 2017, under different RH levels, the relative contribution differences of PM2.5 concentrations and RH to visibility degradation are investigated in depth. On this basis, new multiple nonlinear regressions for low visibility are developed for eight regions of China. The results show that under relatively low RH conditions (<80% or 85%), PM2.5 concentration plays a leading role in visibility changes in China. With the increase in RH (80–90% or 85–95%), the PM2.5 concentration corresponding to the visibility of 10 and 5 km decreases and the contribution of RH becomes increasingly important. When the RH grows to >95%, a relatively low PM2.5 concentration could also lead to visibility decreasing to <5 km. Within this range, the PM2.5 concentration corresponding to the visibility of 5 km in Central China (CC), Sichuan Basin (SCB), and Yangtze River Delta (YRD) is approximately 50, 50, and 30 μg m−3, and that in Beijing-Tianjin-Hebei (BTH) and Guanzhong Plain (GZP) is approximately 125 μg m−3, respectively. Specifically, based on these contribution differences, new multiple nonlinear regression equations of visibility, PM2.5 concentration, temperature, and dew point temperature of the eight regions (Scheme A) are established respectively after grouping the datasets by setting different RH levels (BTH, GZP, and North Eastern China (NEC): RH < 80%, 80 ≤ RH < 90% and RH ≥ 90%; CC, SCB, YRD, and South China Coastal (SCC): RH < 85%, 85 ≤ RH < 95% and RH ≥ 95%; Xinjiang (XJ): RH < 90% and RH ≥ 90%). According to the previous regression methods, we directly established the multiple regression models between visibility and the same factors as a comparison (Scheme B). Statistical results show that the advantage of Scheme A for 5 and 3 km evaluation is more significant compared with Scheme B. For the five low visibility regions (BTH, GZP, CC, SCB, and YRD), RMSEs of Scheme A under visibility <5 and 3 km are 0.77–1.01 and 0.48–0.95 km, 16–43 and 24–57% lower than those of Scheme B, respectively. Moreover, Scheme A reproduced the winter visibility in BTH, GZP, CC, SCB, YRD, and SCC from 2016 to 2020 well. The MAEs, MBs, and RMSEs under visibility < 5 km are 0.44–1.41, −1.33–1.24, and 0.58–2.36 km, respectively. Overall, Scheme A is confirmed to be reliable and applicable for low visibility prediction in many regions of China. This study provides a new visibility parameterization algorithm for the haze–fog numerical prediction system. [ABSTRACT FROM AUTHOR]

Precise estimation of root biomass is important for understanding carbon stocks and dynamics in forests. Traditionally, biomass estimates are based on allometric scaling relationships between stem diameter and coarse root biomass calculated using linear regression (LR) on log-transformed data. Recently, it has been suggested that nonlinear regression (NLR) is a preferable fitting method for scaling relationships. But while this claim has been contested on both theoretical and empirical grounds, and statistical methods have been developed to aid in choosing between the two methods in particular cases, few studies have examined the ramifications of erroneously applying NLR. Here, we use direct measurements of 159 trees belonging to three locally dominant species in east China to compare the LR and NLR models of diameter-root biomass allometry. We then contrast model predictions by estimating stand coarse root biomass based on census data from the nearby 24-ha Gutianshan forest plot and by testing the ability of the models to predict known root biomass values measured on multiple tropical species at the Pasoh Forest Reserve in Malaysia. Based on likelihood estimates for model error distributions, as well as the accuracy of extrapolative predictions, we find that LR on log-transformed data is superior to NLR for fitting diameter-root biomass scaling models. More importantly, inappropriately using NLR leads to grossly inaccurate stand biomass estimates, especially for stands dominated by smaller trees. [ABSTRACT FROM AUTHOR]

The relative growth rate ( R G R n v ) is the standardized measurement of forest growth, whereby excluding the size differences between individuals allows their performance to be compared equally. The R G R n v model was developed using the National Forest Inventory ( NFI ) data on the Daxing'an Mountains, in Northeast China, which contain Dahurian larch (Larix gmelinii Rupr.), white birch (Betula platyphylla Suk.), and mixed coniferous–broadleaf forests. Four predictor variables—i.e., quadratic mean diameter ( D q ), stand basal area (G), average tree height ( H a ), and altitude (A)—and four different methods—i.e., the nonlinear mixed-effects models ( NLME ), three nonlinear quantile regression ( NQR 3 ), five nonlinear quantile regression ( NQR 5 ), and nine nonlinear quantile regression ( NQR 9 ) models—were used in this study. All the models were validated using the leave-one-out method. The results showed that (1) the mixed coniferous–broadleaf forest presented the highest R G R n v ; (2) the R G R n v was negatively correlated with the four predictors, and the heteroscedasticity reduced significantly after the weighting function was integrated into the models; and (3) the quantile regression models performed better than NLME , and NQR 9 outperformed both NQR 3 and NQR 5 . To make more accurate predictions, parameters of the adjusted mixed-effects and quantile regression models should be recalculated and localized using sampled R G R n v in each region and then applied to predict all the other R G R n v of plots. M A P E % indicates the mean absolute percentage error. The values were stable when the sample numbers were greater than or equal to six across the three forest types, which showed relatively accurate and lowest-cost prediction results. [ABSTRACT FROM AUTHOR]

Aimed at the salinization problem of aeolian sand subgrades in desert areas over time, which has not been sufficiently considered in the Ejina region, China, based on multifield coupled numerical simulations with COMSOL Multiphysics and indoor one-dimensional soil column tests, the water and salt migration process in aeolian sand subgrades under a temperature gradient was analysed. The temporal and spatial variations in the temperature, moisture, and salinity in the subgrade salinization process were determined. Selecting a salinization rate of 0.3% accumulated 10 cm below the top of the subgrade as a critical criterion, a regression equation between the salinization time of the aeolian sand subgrade and the initial subgrade moisture content and salinity was established via nonlinear regression analysis. The results show that the temperature inside the subgrade is nonlinear along the depth direction, and the transmission process is accompanied by the attenuation of energy, which establishes a temperature gradient. The moisture and salt contents of the subgrade gradually decrease from bottom to top and are influenced by the pavement coverage effect and temperature gradient. There is a water-salt accumulation zone on the surface of the subgrade. The measured salt content at points in the soil column test agrees well with the simulation results, and the distribution law of water and salt obtained by the model is in good agreement with the water and salt variations in the simultaneous heat and water transfer model. The salinization time of the aeolian sand subgrade is affected by both the initial moisture and salt contents of the subgrade, and the influence of the initial salt content is more notable. The research results have a theoretical guiding significance for the control and prevention of salinization in desert areas. [ABSTRACT FROM AUTHOR]

The forest growth and yield models, which are used as important decision-support tools in forest management, are commonly based on the individual tree characteristics, such as diameter at breast height (DBH), crown ratio, and height to crown base (HCB). Taking direct measurements for DBH and HCB through the ground-based methods is cumbersome and costly. The indirect method of getting such information is possible from remote sensing databases, which can be used to build DBH and HCB prediction models. The DBH and HCB of the same trees are significantly correlated, and so their inherent correlations need to be appropriately accounted for in the DBH and HCB models. However, all the existing DBH and HCB models, including models based on light detection and ranging (LiDAR) have ignored such correlations and thus failed to account for the compatibility of DBH and HCB estimates, in addition to disregarding measurement errors. To address these problems, we developed a compatible simultaneous equation system of DBH and HCB error-in-variable (EIV) models using LiDAR-derived data and ground-measurements for 510 Picea crassifolia Kom trees in northwest China. Four versatile algorithms, such as nonlinear seemingly unrelated regression (NSUR), two-stage least square (2SLS) regression, three-stage least square (3SLS) regression, and full information maximum likelihood (FIML) were evaluated for their estimating efficiencies and precisions for a simultaneous equation system of DBH and HCB EIV models. In addition, two other model structures, namely, nonlinear least squares with HCB estimation not based on the DBH (NLS and NBD) and nonlinear least squares with HCB estimation based on the DBH (NLS and BD) were also developed, and their fitting precisions with a simultaneous equation system compared. The leave-one-out cross-validation method was applied to evaluate all estimating algorithms and their resulting models. We found that only the simultaneous equation system could illustrate the effect of errors associated with the regressors on the response variables (DBH and HCB) and guaranteed the compatibility between the DBH and HCB models at an individual level. In addition, such an established system also effectively accounted for the inherent correlations between DBH with HCB. However, both the NLS and BD model and the NLS and NBD model did not show these properties. The precision of a simultaneous equation system developed using NSUR appeared the best among all the evaluated algorithms. Our equation system does not require the stand-level information as input, but it does require the information of tree height, crown width, and crown projection area, all of which can be readily derived from LiDAR imagery using the delineation algorithms and ground-based DBH measurements. Our results indicate that NSUR is a more reliable and quicker algorithm for developing DBH and HCB models using large scale LiDAR-based datasets. The novelty of this study is that the compatibility problem of the DBH model and the HCB EIV model was properly addressed, and the potential algorithms were compared to choose the most suitable one (NSUR). The presented method and algorithm will be useful for establishing similar compatible equation systems of tree DBH and HCB EIV models for other tree species. [ABSTRACT FROM AUTHOR]

The Axi low-sulfidation (LS) epithermal deposit in northwestern China is the result of geological controls on hydrothermal fluid flow through strike-slip faults. Such controls occur commonly in LS epithermal deposits worldwide, but unfortunately, these have not been quantitatively analyzed to determine their spatial relationships with gold distribution and further guide mineral prospecting. In this study, we conduct a 3D mineral prospectivity modeling approach for the Axi deposit involving 3D geological modeling, 3D spatial analysis, and prospectivity modeling. The spatial analysis of geometric features revealed the gold mineralization trends in convex segments (0–20 m) with a specific distance from fault 2, the lower interface of late volcanic phase, and the upper interface of phyllic alteration with steep slopes (>65°), implying that gold deposition was significantly controlled by the morphological characteristics and distance fields of geologic features. The present alteration–mineralization zone at Axi has a larger width in bending sites (sections No. 35–15 and No. 40–56) than elsewhere, indicating the location of two fluid conduits extending to depth. The prediction-area plots and receiver operating characteristic curves demonstrated that (genetic algorithm optimized support vector regression (GA-SVR)) outperformed multiple nonlinear regression and fuzzy weights-of-evidence, which was proposed as a robust method to solve complicated nonlinear and high-dimensional issues in prospectivity modeling. Our study manifests spatial controls of structure, host rock, and alteration on LS epithermal gold deposition, and highlights the capability of GA-SVR for identifying deposit-scale potential epithermal gold mineralization. [ABSTRACT FROM AUTHOR]