Exponential Regression Equation Research Articles

N2O Emission Factors from Wastewater Treatment Plants Based on Literature Statistics and Model Fitting

The emission of nitrous oxide （N2O） during wastewater treatment cannot be ignored. The analysis of statistical data from literature based on 126 empirical studies revealed that the geographical factors of wastewater treatment plants （WWTPs） had a significant impact on N2O emission factors. However, the N2O emission factors of WWTPs in all regions of the world were generally lower than the Intergovernmental Panel on Climate Change （IPCC） recommended values. In China, the N2O emission factors （in N2O-N/Ninfluent） of WWTPs were approximately 0.000 35-0.065 20 kg·kg-1. Meanwhile, the N2O emission factors of different wastewater treatment processes were also significantly different, especially since the sequencing batch reactor （SBR） process had higher emissions. The use of uniform default emission factors for accounting was prone to overestimate N2O emissions, and it is recommended that countries conduct actual monitoring or modeling studies to develop categorical emission factors suitable for local conditions. In addition, the N2O emission factor based on total nitrogen （TN） removal was weakly negatively correlated with TN removal in 126 empirical data, which was more in line with bioprocessing stoichiometry and could provide an accurate accounting method for N2O. To this end, a digital twin model was developed to dynamically simulate a case anaerobic-anoxic-aerobic （AAO） WWTP to comprehensively quantify the dynamic emission behavior of N2O, which demonstrated that N2O emissions had significant seasonal and daily variability and were only equivalent to 11% of the calculated value of the emission factor based on the IPCC recommendation. Comparing the scatter linear fitting and categorical mean exponential fitting methods, it was found that the latter could more accurately reflect the negative correlation between the N2O emission factors and the TN removal rate, and an exponential regression equation between the average N2O emission factor based on the amount of TN removed and the TN removal rate was further developed to predict the N2O emission. The dynamic simulation and categorical index fitting methods provided in this study are important references for the accurate accounting of N2O emissions in similar WWTPs and provide help for understanding and responding to the N2O emission problems.

Pengaruh Penggunaan Abu Cangkang Terhadap Kuat Tekan Beton

The background of this research is that there is still a lot of oyster shell material that has not been utilized, meanwhile the shell has good mechanical properties as well as concrete. Up to this moment, the shell is not used properly so it needs to studied the use as concrete mixture. By the utilization of this waste is expected to reduce the environmental impact. This study was aims to obtain a regression equation of the compressive strength of concrete using shells, the shells used in this study were oyster shells (ostreidae), tested at 28 days of age with 3 variations in the percentage of the use of shell ash material and with 1 variation of normal concrete. The percentage variations used are 0% variation, 5% variation, 10% variation, and 15% variation. In addition, based on the regression equation, the strong relationship between variables will be obtained which is indicated by the value of r square. The implementation of this research was carried out by carrying out a compressive test of concrete cylinder samples with 3 variations of the water cement rasio (W/C), is W/C 0.4, W/C 0.5, and W/C 0.6. The activity was continued by recording and analyzing data by conducting a regression analysis between the relationship between the percentage of shell use and the compressive strength of concrete. The regression equation used is the exponential regression equation, linear regression, polynomial regression order 2. From the test data, it is found that the regression equation is the best for W/C variation 0.4, obtained polynomial regression equation order 2, y = -0.0351x2 + 0, 3279x + 25.075 with r² = 0.9999, for the W/C variation of 0.5, the second order polynomial regression equation is obtained, y = -0.0702x2 + 0.8062x + 23.406 with r² = 0.8983, and for the W/C variation of 0.6 , the second order polynomial regression equation is obtained, y = -0.0404x2 + 0.4008x + 19.074 with a value of r² = 0.9167..

Structure-Function Relationship in Keratoconus: Spatial and Depth Vision.

The purpose of this study was to determine changes in spatial and depth vision with increasing severity of keratoconus and to model the structure-function relationship to identify distinct phases of loss in visual function with disease severity. Best-spectacle corrected, monocular high-contrast visual acuity, contrast sensitivity function (CSF) and stereoacuity of 155 cases (16-31years) with mild to advanced bilateral keratoconus was determined using standard psychophysical tests. Disease severity was quantified using the multimetric D-index. The structure-function relationship was modeled using linear, positive exponential, negative exponential, and logistic nonlinear regression equations. The logistic regression model explained the highest proportion of variance for spatial vision, without bias in the residual plots (R2 ≥ 66%, P < 0.001). Visual acuity showed a distinct ceiling phase and a steeper loss rate with increasing D-index (1.8units/D-index) in this model. The area under the CSF lacked this ceiling phase and had a shallower loss rate (0.28units/D-index). Stereoacuity loss with D-index was poorly explained by all models tested (P ≤ 0.2). Cases with lower and bilaterally symmetric D-index had better stereoacuity (181.6-376 arc seconds) than those with higher D-index (>400 arc second); both were significantly poorer than controls (approximately 30 arc second). Vision loss in keratoconus varies with the visual function parameter tested. Contrast sensitivity may be an earlier indicator of spatial vision loss than visual acuity. Depth perception is significantly deteriorated from very early stages of the disease. The study outcomes may be used to forecast longitudinal vision loss in keratoconus and to apply appropriate interventions for timely preservation/enhancement of vulnerable visual functions.

Development and validation of a predictive model for fetal cerebral maturation using ultrasound for fetuses with normal growth and fetal growth restriction.

Investigation of fetal cerebral maturation (FCM) is necessary and important to provide crucial prognostic information for normal and high-risk fetuses. The study aimed to develop a valid and quantitative predictive model for assessing FCM using ultrasound and validate the model for fetuses with normal and restricted growth. This was a multicenter prospective observational study. Fetuses with normal growth recruited from a university teaching hospital (Center 1) and a municipal maternal unit (Center 2) were included in the training set and external validation set 1, respectively. The 124 growth-restricted fetuses enrolled in Center 1 were included in validation set 2. FCM was used to describe the gestational age (GA) in this study. The model was developed based on the sum of fetal cranial parameters (total fetal cranial parameters), including head circumference (HC) and depths of the insula (INS) and sylvian fissure (SF), parieto-occipital fissure (POF), and calcarine fissure (CF). A regression model, constructed based on total fetal cranial parameters and predicted GA, was established using the training set and validated using external validation set 1 and validation set 2. The intra- and interobserver intraclass correlation coefficients for HC, and depths of the INS and SF, POF, and CF were >0.90. An exponential regression equation was used to predict FCM: predicted GA of FCM (weeks) =11.16 × exp (0.003 × total fetal cranial parameters) (P<0.001; adjusted R2=0.973), standard error of estimate, 0.67 weeks. The standard error of the predicted GA of FCM from the model was ±4.7 days. In the validation set 1, the mean standard error of the developed prediction model for FCM was 0.97 weeks. The predictive model showed that FCM was significantly delayed in validation set 2 (2.10±1.31 weeks, P<0.001), considering the GA per the last menstrual period. The predictive performance of the FCM model developed in this study was excellent, and the novel model may be a valuable investigative tool during clinical implementation.

Warming Stimulated Soil Respiration in a Subalpine Meadow in North China

In order to explore the response of soil respiration in grassland to global warming, we carried out a warming experiment with open top chambers (OTCs) in the subalpine meadow, Mount Wutai in north China. Our results showed in the subalpine meadow across 2 500-2 700 m above the sea level (ASL), with OTCs, soil respiration increased by 2.00 μmol·m-2·s-1 as soil temperature increased by 1.25 ℃ on average. Warming decreased soil moisture over the experiment periods except in October 2019 when snow melted in OTCs. Warming effect on soil respiration peaked at 178.31% in October 2019. In control and warming treatment, based on exponential regression equations, soil temperature alone accounted for 85.3% and 61.2% of soil respiration variation, respectively. In control treatment soil moisture alone explained 23.2% of soil respiration variation based on the power regression equation while in warming treatment they were not significantly correlated with each other. The response of soil respiration to warming relied on altitudes as well as the time of the year, but was not inhibited by soil moisture, labile carbon pool, and available nitrogen. We concluded soil temperature was the main factor influencing soil respiration, and global warming would stimulate soil respiration in the subalpine meadows of Mount Wutai in the future. Our analysis provided new data on characteristics and mechanisms of the response of soil respiration to warming, and helped to further understand the relationship between carbon cycle and climate change.

Characteristics of Disaster Losses Distribution and Disaster Reduction Risk Investment in China from 2010 to 2020

China is one of an increasing number of countries in the world that is suffering from frequent and severe natural disasters, which cause serious loss of life. The Chinese government has set up a special financial fund for natural disaster mitigation and reduction. Therefore, based on the financial expenditure data and disaster losses data obtained from ministries of emergency management and the China Statistical Yearbook, we analyzed the spatio-temporal distribution of natural disaster losses at the economic zonal scale during 2010–2020, and then evaluated the efficiency of disaster mitigation and reduction using a DEA model. The results showed that the natural disaster losses decreased significantly in most provinces from 2010 to 2020. The distribution of precipitation is extremely uneven (more in the southeast and less in the northwest). Moreover, the Central and Western Economic Zones are the most earthquake-prone regions in China, especially Xinjiang, Tibet, Sichuan, Yunnan and Gansu. Among all natural disasters, floods were the leading natural disasters, causing the most severe losses in China on the national scale. Furthermore, the cities with higher comprehensive efficiency, mean the ratio between the effects and funding on disaster mitigation and reduction, were either economically developed or geographically large and sparsely populated. Finally, we used an exponential regression equation model to explore the relationship between financial input and direct economic losses caused by natural disasters in 2019 and 2020; we found that there is a negative correlation between the financial investment and the direct economic losses. In conclusion, it is necessary to improve the technology of natural disaster mitigation and reduction and to adjust the scale of investment according to the actual situation of each region and the different disasters in China. This paper aims to provide relevant experience and basis for China’s comprehensive disaster mitigation and reduction work.

Innovative nanostructured magnetite/wool/polysiloxane composite as magnetic adsorbent for oil spill removal

We report herein the synthesis and characterization of a new nanostructured composite (Wool-Fe 3 O 4 -Polysiloxane) aimed to be applied as a magnetic adsorbent for oil spill cleanup. The synthesized composite was characterized by the physical–chemical instrumental techniques (XRD, FTIR, SEM-EDX, and VSM). The hysteresis loops from the VSM measurement disclosed a magnetic saturation value of M S =5.47 emu/g that was sufficient to induce magnetic properties to the composite material. The produced magnetic adsorbent showing hydrophobic properties was successfully tested for the sorption of petroleum products. Kinetic data of oily liquids retention and dripping were modeled by using the exponential and hyperbolic regression equations with three parameters. The hyperbolic kinetic model suggested higher maximal sorption capacity at time zero (t=0), that is, 14.486, 12.896, and 3.501 g/g, for motor oils 15W, 5W, and n-dodecane, respectively. The advanced recovery (80–92%) of retained oils from the spent sorbent was done by centrifugation.

Litter Decomposition of Qinghai Spruce (Picea crassifolia) Is Dependent on Mn Concentration in the Qilian Mountains, Northwest China

The factors determining litter decomposition incorporated into C and nutrient cycles were examined as part of a broader study investigating the biogeochemical cycle in forest ecosystems. Litter was collected from five altitudes of Qinghai spruce (Picea crassifolia) woodland stands in the Qilian Mountains and placed in litterbags. These litterbags were installed at the crown center (CC) and crown edge (CE) at different altitudes in Qinghai spruce forests during the growing season to study the effect of litter substrate quality on litter decomposition. Results indicate that at varying altitudes in the growing season, the initial mass loss rate and initial decomposition rate of Qinghai spruce litter showed a nonlinear relationship with altitude. The Olson exponential regression equation showed that the decomposition coefficient (k) was the largest at 3050 m (k = 0.709), and the decomposition coefficient (k) was the smallest at 3250 m (k = 0.476). Meanwhile, the initial decomposition rate was highly correlated with initial litter Ca and Mn concentrations. At the CC and CE at different altitudes in the growing season, the initial mass loss rate of CE was significantly higher than that of CC (p &lt; 0.01), and the initial decomposition rate of CE was markedly faster than that of CC (p &lt; 0.01). The Olson exponential regression equation showed that CE’s decomposition coefficients (k) were larger than those of CC. The initial decomposition rate of CE was highly correlated with initial litter C and Mn concentrations. However, the initial decomposition rate at CC was independent of the litter substrate quality. Finally, we realize that litter decomposition in the early stages is not ultimately determined by a single common factor, but rather the result of multiple factors working together in different orders and strengths. The results lay a foundation for understanding the process and mechanism of litter decomposition in the alpine mountain forest ecosystem and further understanding the structure and function of the ecosystem.

Short-term responses of the anammox process to Ni(II): nitrogen removal, mechanisms and inhibition recovery

Anaerobic ammonia oxidizing (anammox) has already been recognized as an innovative and economical nitrogen removal technology. However, the effect of heavy metals on anammox bacteria in aquatic ecosystem remains largely unknown. Ni(II) is a common kind of heavy metals detected in industrial wastewater and municipal sewage treatment plants. Hence, the responses of the anammox process to Ni(II) were studied here. The results showed that anammox was the dominant reaction with Ni(II) concentrations no more than 25 mg/L. 1 mg/L of Ni(II) addition promoted nitrogen removal by anammox. The higher the Ni(II) concentrations and longer exposure time, the more inhibition for anammox bacteria was gotten. The IC50 of Ni(II) to anammox was determined as 83.86 mg/L by an exponential regression equation. The inhibition of Ni(II) on anammox activity was mainly attributed to intracellular accumulation Ni(II) inhibition to HDH activity. Two times increase of IC50 after 4 times circles of domestication suggests multiple intermittent domestication can increase the tolerance of anammox bacteria to Ni(II). EDTA washing can eliminate the inhibition of anammox activity by Ni(II) with Ni(II) addition no more than 25 mg/L.

Experimental study for optimizing superfinishing process parameters of high-quality alloy bearing steel

Superfinishing is one of the methods of high-quality surface machining of elements subjected to high surface wear. It is used for machining external and internal cylindrical surfaces using various models of tools. This experimental study was aimed at determining the effect of machining parameters on surface roughness of high-quality alloy bearing steel. The factors considered were angle of crosshatch pattern (realized by the rotational speed at constant velocity and oscillation), machining time, and pressure of the tool on the machined surface. The experiment was carried out according to the analysis planned for two tools with granulations of 500 and 800. The polynomial and exponential regression equations for subsequent roughness and performance parameters were determined statistically. The multidimensional correlations based on the t-student distribution were established. The results showed that the optimum surface quality depended on the process parameters: grain size, machining time, crosshatch angle, and the contact pressure. The time at which the machining process starts to stabilize with a steady surface roughness was determined to be 120 s. The maximum enhancement of surface roughness was 75% for crosshatch angle of 13°, contact pressure of 0.21 MPa, and granulation of 800. For both 500 and 800 granulations, the diameter loss was in the range of 1–12 microns.

Similarity characteristics of geometric scaling matrix cooling channels in turbine blade

• Geometric similarity characteristics of matrix cooling structure are investigated. • The prediction of cooling performance is performed by machine learning. • The cooling performance increases exponentially with the increasing scaling factor. Gas turbine blades require high-performance cooling structures to meet the increasing turbine inlet temperature requirements. The matrix cooling structure has high heat transfer performance, but at the same time brings a large resistance loss. In basic research, the cooling structure is usually modeled according to the similarity criterion, but there is doubt about the generalization of the results obtained from the scaled model to the real-size cooling structure in engineering applications. Therefore, numerical simulation was applied to study the similarity characteristics of matrix cooling performance and the effects of geometric scaling factor in this paper. Through data fitting, it was found that the heat transfer performance and resistance performance increased exponentially with the increase of the scaling factor. As geometric scaling factor shrank 10 times, the heat transfer and resistance loss decreased by 21.9% and 34.7% respectively. The heat transfer and resistance loss of each sub-duct were also markedly affected by the geometric scaling. In small scaling factor cases, the heat transfer was weak in the downstream areas of the cooling channel, and there was an obvious gap in resistance loss for each sub-duct. Due to the same geometric characteristics, the flow characteristics of the channels were less affected by geometric scaling factor, but the relative velocity increased with the decrease of the scaling factor. The exponential regression equations of the heat transfer and resistance with respect to the scaling factor were established by machine learning, providing an accurate and appropriate prediction for engineering application.

Study on the air resistance of pigs in groups based on open-source CFD code: Influence of stocking density and live weight

Animal stocking density (STKD) and live weight (LIVW) are the main factors determining the air resistance in ventilation system. To study the impact of LIVW (30, 45, 60, 75, 90, 105, 120 and 135 kg) and STKD (0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 2.0, and 2.6 pig m −2 ) on air resistance of pig groups, an open-source computational fluid dynamics (CFD) code, OpenFOAM, was adopted. Firstly, pressure drop of tube bundles which included 15 staggered tubes with a diameter of 0.40 m, equivalent to pig groups with 50-kg and 0.9 pig m −2 , were simulated for the accuracy of CFD models which were then used for calculating pig groups' air resistance for horizontal (HD) and vertical (VD) airflow directions. The validation of CFD models was based on semi-empirical equations for the tube bundle. The results indicated that: 1) The CFD model (realisable k-ε model, 10 boundary layers with 0.04 m total thickness) was reliable for pressure drop of complex bluff bodies with an average relative error of 6.80%. 2) The pig groups' friction factor for HD or VD almost increased with the rising STKD or LIVW, and regression equations were established to estimate friction factors according to LIVW and STKD of pig groups. 3) Regarding to pig groups’ air resistance, STKD is much more important than LIVW for VD, but LIVW and STKD have almost the same importance for HD. The interaction of LIVW and STKD for HD and VD has little effect. • OpenFOAM was reliable to calculate the pressure drop of complex bluff bodies. • Varied mass and stocking densities affect the air resistance of pig groups. • Pig groups' friction factors can be predicted by exponential regression equations.

Determination of Moisture Content and Shrinkage Strain during Wood Water Loss with Electrochemical Method.

Moisture content and shrinkage strain are essential parameters during the wood drying process. The accurate detection of these parameters has very important significance for controlling the drying process and minimizing drying defects. The presented study describes an electrochemical method to determine wood moisture content and shrinkage strain during drying, and the accuracy of this method is also evaluated. According to the results, the electrical resistance of the samples increased with the decrease in wood moisture content. As the moisture content changed from 42% to 12%, the resistance increased from 1.0 × 107 Ω to 1.2 × 108 Ω. A polynomial fitting curve was fitted with a determination coefficient of 0.937 to describe the relationship between moisture content and electrical resistance. In addition, both the shrinkage strain and resistance change rate increased with the decrease in wood moisture content, especially for the moisture content range of 23% to 8%, where the shrinkage strain and resistance change rate increased by 4% and 30%, respectively. The shrinkage strain increased exponentially with the increase in the resistance change rate; thereby, an exponential regression equation was proposed with a determination coefficient of 0.985, expressing the correlation between the two. This demonstrates the feasibility of the electrochemical method for measuring wood moisture content and shrinkage strain.

Characterization of Recently Planted Coffee Cultivars from Vegetation Indices Obtained by a Remotely Piloted Aircraft System

Brazil is the main producer and exporter and the second-largest consumer of coffee in the world, and Remotely Piloted Aircraft Systems stands out as an efficient remote detection technique applied to the study and mapping of crops. The objective of this study was to characterize three recently planted cultivars of Coffea arabica L. The study area is in Minas Gerais, Brazil, with a coffee plantation of the initial age of 5 months. The temporal behavior was determined based on monthly mean values. The spectral profile was obtained with mean values of the last month of dry and rainy periods. The statistical differences were obtained based on the non-parametric test of multiple comparisons. The estimation of the exponential equation was obtained through the Spearman correlation coefficient of determination and root mean square error. It was concluded that the seasons influence the behavior and development of cultivars, and significant statistical differences were detected for the variables, except for the chlorophyll variable. Due to the proximity and overlap of the reflectance values, spectral bands were not used to individualize cultivars. A correlation between the vegetation indices and leaf area index was observed and the exponential regression equation was estimated for each cultivar under study.

Linear Relationship between Temperature and the Apparent Reaction Rate Constant of Hydroxyl Radical with 4-chlorobenzoic Acid

ABSTRACT 4-Chlorobenzoic acid (p-CBA) is frequently used as a hydroxyl radical (HO·) probe substance in studies of ozonation and advanced oxidation processes. However, the temperature dependence of the reaction between HO· and p-CBA remains unclear. In this context, we identified the relationship between temperature ( T , K) and the apparent second-order reaction rate constant of HO· with p- CBA ( k H O ⋅ , p − C B A T , M−1 s−1): 10 10 . They were measured by a novel competitive method using 2-methylpropan-2-ol (tert-butyl alcohol) as a reference substance in the range of 1.0–40.0℃. The linear regression equation was more appropriate than the exponential regression equation to express this relationship. More generally, our simulation shows that the linear regression equation can be more accurate than the exponential regression equation to express the relationship between temperature and apparent reaction rate constants of HO.

Economic Analysis of Arrivals and Prices of Pomegranate in Solapur District of Maharashtra

"The price behaviour of different agricultural commodities and the responsiveness of market arrivals to the price movement to reduce the gap in market arrival mark the need for analyzing the market arrivals and prices. This study is based on the secondary data of 15 years from 2005-06 to 2019-20 which was collected from APMC Solapur and APMC Pandharpur in Solapur district. For the analytical framework, the Exponential Regression Equation was used to study the trend in arrivals and prices. During the study period, in Solapur market, arrival was highest in August and the lowest in April. Pandharpur market observed maximum arrival in July whereas the lowest arrival in February. In terms of prices, Solapur market witnessed maximum price per quintal in September whereas minimum price per quintal in May. Pandharpur market recorded highest price per quintal of Pomegranate in February and the lowest price per quintal in November. During the study period, Solapur market witnessed highest positive growth rate whereas Pandharpur market reported a non-significant growth in terms of arrival among the selected markets. In terms of prices, Solapur market showed a positive and significant growth rate while Pandharpur market observed a negative and non-significant growth during the study period. The main season for arrivals of the pomegranate in both markets was from August to December."

Big Data Information under Proportional Hazard Mathematical Model in Analysis of Hepatitis B Virus Infection Data of Patients with Interventional Liver Cancer through Antiviral Therapy of Entecavir.

The objective of this study was to analyze the application of proportional hazard mathematical model (PHMM) in Hepatitis B Virus (HBV) infection analysis of interventional liver cancer patients treated with entecavir, so as to provide data support for clinical diagnosis and treatment. Based on the survival analysis, the treatment factor x was undertaken as an independent variable to perform linear regression. The regression model took the hazard rate function as the dependent variable to establish an exponential regression equation to construct a PHMM. 136 patients with primary liver cancer receiving interventional chemoembolization combined with the drug (entecavir) were selected as the experimental group, who were in the computer gene expression omnibus (GEO). 87 patients with primary liver cancer who underwent interventional chemoembolization therapy without antiviral treatment were taken as the control group. The PHMM was adopted for comprehensive analysis. In addition, the factors affecting the virological response to antiviral therapy were analyzed using the multiple logistic regression. The results revealed that HBV deoxyribonucleic acid (DNA) negative conversion rate, Hepatitis B e-Antigen (HBeAg) negative conversion rate, and HBeAg serological conversion rate in the experimental group were much higher than those in the control group (P < 0.05). HBV DNA level and proportion of HBsAg <100 IU/mL in the experimental group were much lower than those in the control group (P < 0.05). The virological breakthrough rate and incidence of adverse events at week 24 in the experimental group were greatly lower than those in the control group (P < 0.05). The adverse virological response of patient was positively correlated with HBV DNA load and HBeAg status and negatively correlated with alanine aminotransferase (ALT) level (P < 0.05). Therefore, entecavir can significantly inhibit HBV DNA replication in patients with liver cancer, showing high antiviral effect. High baseline HBV DNA load, positive HBeAg, and low baseline alanine aminotransferase levels were independent risk factors for adverse virology response to entecavir antiviral therapy, which provided a reference for the selection of antiviral drugs for HBV infection.

Quantifying lung and diaphragm morphology using radiographs in normative pediatric subjects, and predicting CT-derived lung volume.

To quantify the effect of age on two-dimensional (2D) radiographic lung and diaphragm morphology and determine if 2D radiographic lung measurements can be used to estimate computer tomography (CT)-derived lung volume in normative pediatric subjects. Digitally reconstructed radiographs (DRRs) were created using retrospective chest CT scans from 77 pediatric male and female subjects aged birth to 19 years. 2D lung and diaphragm measurements were made on the DRRs using custom MATLAB code, and Spearman correlations and exponential regression equations were used to relate 2D measurements with age. In addition, 3D lung volumes were segmented using CT scans, and power regression equations were fitted to predict each lung's CT-derived volume from 2D lung measurements. The coefficient of determination (R2 ) and standard error of the estimate (SEE) were used to assess the precision of the predictive equations with p < .05 indicating statistical significance. All 2D radiographic lung and diaphragm measurements showed statistically significant positive correlations with age (p < .01), including lung major axis (Spearman rho ≥ 0.90). Precise estimations of CT-derived lung volumes can be made using 2D lung measurements (R2 ≥ 0.95), including lung major axis (R2 ≥ 0.97). The reported pediatric age-specific reference data on 2D lung and diaphragm morphology and growth rates could be clinically used to identify lung and diaphragm pathologies during chest X-ray evaluations. The simple, precise, and clinically adaptable radiographic method for estimating CT-derived lung volumes may be used when pulmonary function tests are not readily available or difficult to perform.

The Risk Quantification Evaluation Strategy for the Distribution Line based on EMLR

The electric power distribution grid directly serves the ordinary users, the stability and reliability of which are crucial to production and life. The traditional operation and maintenance of the distribution grid is based on experience and cannot scientifically predict the state of line faults. In this paper, stepwise regression is used to screen out 8 key influencing factors of line faults, and principal component analysis is performed to find the risk value of each line; so that establishes multiple linear and exponential regression equations for the line risk value. According to the existing data, the risk of the line is predicted, and the GABP neural network model is built so as to accurately predict the risk value of the line.

INFLUENCE OF SOIL TYPE ON WOOD DENSITY AND MEAN ANNUAL INCREMENT IN TWO COMMERCIAL Eucalyptus CLONES

In forest plantations, the productive potential of a given site results from the interaction between genetic materials and environmental conditions. Both are fundamental factors for wood quality and Mean Annual Increment. Knowing the performance of different Eucalyptus urophylla commercial clones under different soil conditions is important for forest planning. Our objective is to evaluate the infuence of soil types on Wood Basic Density and Mean Annual Increment - MAI in two E. urophylla clones. We analyzed the Wood Basic Density of 3- to 8-year-old trees and MAI from two clones grown in four soil types in 14 localities of São Paulo state. ANOVA and Tukey test at 5% probability were performed to test the effect of soil type on Wood Basic Density and MAI. Linear and exponential regression equations were performed between Wood Basic Density x MAI and between MAI x Clay Content, respectively. In general, some soils showed signifcant differences in Wood Basic Density, possibly from climatic variation among sites. Also, the MAI increased exponentially with increase of Soil Clay Content (up to about 25 to 35% of clay), but no correlation was observed between MAI and Wood Density.