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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]

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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]