Evaluation of energy values of high-fiber dietary ingredients with different solubility fed to growing pigs using the difference and regression methods

The objective of this study was to compare the energy values of high-fiber dietary ingredients with different solubility(sugar beet pulp[SBP]and defatted rice bran[DFRB])in growing pigs using the difference and the regression methods.A total of 21 barrows(initial BW,40.5±1.2 kg)were assigned to 3 blocks with BW as a blocking factor,and each block was assigned to a 7×2 incomplete Latin square design with 7 diets and two 13-d experimental periods.The 7 experimental diets consisted of a corn-soybean meal basal diet and 6 additional diets containing 10%,20%,or 30%SBP or DFRB in the basal diet,respectively.Each of the exper-imental periods lasted 12 d,with a 7 d dietary adaptation period followed by 5-d total fecal and urine collection.Results showed that the digestible energy(DE)and metabolizable energy(ME)of the SBP determined by the difference method with different inclusion levels(10%,20%,or 30%)were 2,712 and 2,628 kcal/kg,2,683 and 2,580 kcal/kg,and 2,643 and 2,554 kcal/kg DM basis,respectively.The DE and ME in the DFRB evaluated by the difference method with 3 different inclusion levels were 2,407 and 2,243 kcal/kg,2,687 and 2,598 kcal/kg,and 2,630 and 2,544 kcal/kg DM basis,respectively.Different inclusion levels had no effects on the energy values of each test ingredient estimated by the difference method.The DE and ME of the SBP and the DFRB estimated by the regression method were 2,562 and 2,472 kcal/kg and 2,685 and 2,606 kcal/kg DM basis,respectively.The energy values of each ingredient determined by the regression method were similar to the values estimated by the difference method with the 20%or 30%inclusion level.However,the energy values of the SBP and DFRB estimated by the difference method with the 10%inclusion level were inconsistent with the values determined by the regression method(P<0.05).In conclusion,the regression method was a robust indirect method to evaluate the energy values for high-fiber ingredients with different solubility in growing pigs.If the number of experimental animals was limited,the difference method with a moderate inclusion level(at least 20%)of the test high-fiber ingredient in the basal diet could be applied to substitute the regression method.

Nuclear charge radius predictions by kernel ridge regression with odd-even effects

The extended kernel ridge regression(EKRR)method with odd-even effects was adopted to improve the description of the nuclear charge radius using five commonly used nuclear models.These are:(i)the isospin-dependent A^(1∕3) formula,(ii)relativistic continuum Hartree-Bogoliubov(RCHB)theory,(iii)Hartree-Fock-Bogoliubov(HFB)model HFB25,(iv)the Weizsacker-Skyrme(WS)model WS*,and(v)HFB25*model.In the last two models,the charge radii were calculated using a five-parameter formula with the nuclear shell corrections and deformations obtained from the WS and HFB25 models,respectively.For each model,the resultant root-mean-square deviation for the 1014 nuclei with proton number Z≥8 can be significantly reduced to 0.009-0.013 fm after considering the modification with the EKRR method.The best among them was the RCHB model,with a root-mean-square deviation of 0.0092 fm.The extrapolation abilities of the KRR and EKRR methods for the neutron-rich region were examined,and it was found that after considering the odd-even effects,the extrapolation power was improved compared with that of the original KRR method.The strong odd-even staggering of nuclear charge radii of Ca and Cu isotopes and the abrupt kinks across the neutron N=126 and 82 shell closures were also calculated and could be reproduced quite well by calculations using the EKRR method.

Data-driven methods for predicting the representative temperature of bridge cable based on limited measured data

Cable-stayed bridges have been widely used in high-speed railway infrastructure.The accurate determination of cable’s representative temperatures is vital during the intricate processes of design,construction,and maintenance of cable-stayed bridges.However,the representative temperatures of stayed cables are not specified in the existing design codes.To address this issue,this study investigates the distribution of the cable temperature and determinates its representative temperature.First,an experimental investigation,spanning over a period of one year,was carried out near the bridge site to obtain the temperature data.According to the statistical analysis of the measured data,it reveals that the temperature distribution is generally uniform along the cable cross-section without significant temperature gradient.Then,based on the limited data,the Monte Carlo,the gradient boosted regression trees(GBRT),and univariate linear regression(ULR)methods are employed to predict the cable’s representative temperature throughout the service life.These methods effectively overcome the limitations of insufficient monitoring data and accurately predict the representative temperature of the cables.However,each method has its own advantages and limitations in terms of applicability and accuracy.A comprehensive evaluation of the performance of these methods is conducted,and practical recommendations are provided for their application.The proposed methods and representative temperatures provide a good basis for the operation and maintenance of in-service long-span cable-stayed bridges.

Expanding the Scope of Multivariate Regression Approaches in Cross-Omics Research

Recent technological advancements and developments have led to a dramatic increase in the amount of high-dimensional data and thus have increased the demand for proper and efficient multivariate regression methods.Numerous traditional multivariate approaches such as principal component analysis have been used broadly in various research areas,including investment analysis,image identification,and population genetic structure analysis.However,these common approaches have the limitations of ignoring the correlations between responses and a low variable selection efficiency.Therefore,in this article,we introduce the reduced rank regression method and its extensions,sparse reduced rank regression and subspace assisted regression with row sparsity,which hold potential to meet the above demands and thus improve the interpretability of regression models.We conducted a simulation study to evaluate their performance and compared them with several other variable selection methods.For different application scenarios,we also provide selection suggestions based on predictive ability and variable selection accuracy.Finally,to demonstrate the practical value of these methods in the field of microbiome research,we applied our chosen method to real population-level microbiome data,the results of which validated our method.Our method extensions provide valuable guidelines for future omics research,especially with respect to multivariate regression,and could pave the way for novel discoveries in microbiome and related research fields.

Correlations between direct and indirect strength test methods

The difficulties associated with performing direct compression strength tests on rocks lead to the development of indirect test methods for the rock strength assessment. Indirect test methods are simple, more economical, less time-consuming, and easily adaptable to the field. The main aim of this study was to derive correlations between direct and indirect test methods for basalt and rhyolite rock types from Carlin trend deposits in Nevada. In the destructive methods, point load index, block punch index, and splitting tensile strength tests are performed. In the non-destructive methods, Schmidt hammer and ultrasonic pulse velocity tests are performed. Correlations between the direct and indirect compression strength tests are developed using linear and nonlinear regression analysis methods. The results show that the splitting tensile strength has the best correlation with the uniaxial compression strength.Furthermore, the Poisson's ratio has no correlation with any of the direct and indirect test results.

Evaluation of cloud seeding project in Yazd Province of Iran using historical regression method(case study:Yazd 1 cloud seeding project,1999)

In this research, the result of the cloud seeding over Yazd province during three months of February, March and April in 1999 has been evaluated using the historical regression method. Hereupon, the rain-gages in Yazd province as the target stations and the rain-gages of the neighboring provinces as the control stations have been selected. The rainfall averages for the three aforementioned months through 25 years (1973-1997) in all control and target stations have been calculated. In the next step, the correlations between the rainfalls of control and target stations have been estimated about 75%, which indicates a good consistency in order to use the historical regression. Then, through the obtained liner correlation equation between the control and target stations the precipitation amount for February, March and April in 1999, over the target region (Yazd province) was estimated about 27.57 mm, whiles the observed amount was 34.23 mm. In fact the precipitation increasing around 19.5% over Yazd province confirmed the success of this cloud seeding project.

Determination of Compositions and Stability Constants of Holmium and Yttrium Complexes with Tribromoarsenazo by Linear Regression Method

According to the appearing of isosbestic point in the absorption spectra of Ho/Y-Tribromoarsenazo (TBA)systems,the complexation reaction is considered to be M+nL=ML_n.A method has been proposed based on it for calculating the mole fraction of free complexing agent in the solutions from spectral data.and two linear regression formula have been introduced to determine the composition,the molar absorptivity,the conditional stability constant of the complex and the concentration of the complexing agent. This method has been used in Ho-TBA and Y-TBA systems.Ho^(3+)and Y^(3+)react with TBA and form 1: 2 complexes in HCl-NaAc buffer solution at pH 3.80.Their molar absorptivities determined are 1.03×10~8 and 1.10×10~8 cm^2·mol^(-1),and the conditional stability constants(logβ_2)are 11.37 and 11.15 respectively.After considering the pH effect in TBA complexing,their stability constants(log β_2^(ahs))are 43.23 and 43.01. respectively.The new method is adaptable to such systems where the accurate concentration of the complexing agent can not be known conveniently.

Determination of Composition and Stability Constant of Praseodymium(Pr^(3+))Complex with Tribromoarsenazo(TBA)by Dual-Series Linear Regression Method

A new method,dual-series linear regression method,has been used to study the complexation equilibrium of praseodymium(Pr^(3+))with tribromoarsenazo(TBA)without knowing the accurate concentra- tion of the complexing agent TBA.In 1.2 mol/L HCl solution, Pr^(3+)reacts with TBA and forms 1:3 com- plex,the conditional stability constant(lgβ_3)of the complex determined is 15.47,and its molar absorptivity(ε_3^(630))is 1.48×10~5 L·mol^(-1)·cm^(-1).

Prediction of Link Failure in MANET-IoT Using Fuzzy Linear Regression

A Mobile Ad-hoc NETwork(MANET)contains numerous mobile nodes,and it forms a structure-less network associated with wireless links.But,the node movement is the key feature of MANETs;hence,the quick action of the nodes guides a link failure.This link failure creates more data packet drops that can cause a long time delay.As a result,measuring accurate link failure time is the key factor in the MANET.This paper presents a Fuzzy Linear Regression Method to measure Link Failure(FLRLF)and provide an optimal route in the MANET-Internet of Things(IoT).This work aims to predict link failure and improve routing efficiency in MANET.The Fuzzy Linear Regression Method(FLRM)measures the long lifespan link based on the link failure.The mobile node group is built by the Received Signal Strength(RSS).The Hill Climbing(HC)method selects the Group Leader(GL)based on node mobility,node degree and node energy.Additionally,it uses a Data Gathering node forward the infor-mation from GL to the sink node through multiple GL.The GL is identified by linking lifespan and energy using the Particle Swarm Optimization(PSO)algo-rithm.The simulation results demonstrate that the FLRLF approach increases the GL lifespan and minimizes the link failure time in the MANET.

Prediction of COVID-19 Confirmed Cases Using Gradient Boosting Regression Method

The fast spread of coronavirus disease(COVID-19)caused by SARSCoV-2 has become a pandemic and a serious threat to the world.As of May 30,2020,this disease had infected more than 6 million people globally,with hundreds of thousands of deaths.Therefore,there is an urgent need to predict confirmed cases so as to analyze the impact of COVID-19 and practice readiness in healthcare systems.This study uses gradient boosting regression(GBR)to build a trained model to predict the daily total confirmed cases of COVID-19.The GBR method can minimize the loss function of the training process and create a single strong learner from weak learners.Experiments are conducted on a dataset of daily confirmed COVID-19 cases from January 22,2020,to May 30,2020.The results are evaluated on a set of evaluation performance measures using 10-fold cross-validation to demonstrate the effectiveness of the GBR method.The results reveal that the GBR model achieves 0.00686 root mean square error,the lowest among several comparative models.

Does Environmental Regulation Increase Employment? Based on the SCM and RCM Methods

Whether environmental regulation can increase employment is still controversial in academic circles around the world.An important reason lies in the validity of an empirical method.Using China’s inter-provincial panel data from 2003 to 2015 and the synthetic control method(SCM),this paper focuses on a test that was carried out on the basis of a quasi-natural experiment of the 2007 Emission Trading Pilot(ETP)policy.The test results show that the ETP policy has increased the average employment level by 3.25 percentage points and passed a robustness test.The robustness test using the regression control method(RCM)shows that the average employment level has risen by 3.21 percentage points.This means that the ETP policy has significantly increased employment.The paper also puts forward three policy recommendations:optimizing the trading system for emissions rights,encouraging companies to carry out cleaner production and innovation,and incorporating environmental performance assessments.

Subjective Evaluation of Clothing Comfort and Their Correlation with Laboratory Test

A series of polyester, cotton, and polyester/cotton blend fabrics were evaluated in a subjective wear. Subjective trial data were correlated with the results of in-house laboratory tests with the regression methods. Experimental results indicate that clothing-comfort assessment by human perception provides a sound basis for comparison of apparel garments under combined microclimates and human-activity levels of

Spatio-temporal dynamics of vegetation in Jungar Banner of China during 2000–2017

It is known that the exploitation of opencast coal mines has seriously damaged the environments in the semi-arid areas.Vegetation status can reliably reflect the ecological degeneration and restoration in the opencast mining areas in the semi-arid areas.Long-time series MODIS NDVI data are widely used to simulate the vegetation cover to reflect the disturbance and restoration of local ecosystems.In this study, both qualitative(linear regression method and coefficient of variation(CoV)) and quantitative(spatial buffer analysis, and change amplitude and the rate of change in the average NDVI) analyses were conducted to analyze the spatio-temporal dynamics of vegetation during 2000–2017 in Jungar Banner of Inner Mongolia Autonomous Region, China, at the large(Jungar Banner and three mine groups) and small(three types of functional areas: opencast coal mining excavation areas, reclamation areas and natural areas) scales.The results show that the rates of change in the average NDVI in the reclamation areas(20%–60%) and opencast coal mining excavation areas(10%–20%) were considerably higher than that in the natural areas(<7%).The vegetation in the reclamation areas experienced a trend of increase(3–5 a after reclamation)-decrease(the sixth year of reclamation)-stability.The vegetation in Jungar Banner has a spatial heterogeneity under the influences of mining and reclamation activities.The ratio of vegetation improvement area to vegetation degradation area in the west, southwest and east mine groups during 2000–2017 was 8:1, 20:1 and 33:1, respectively.The regions with the high CoV of NDVI above 0.45 were mainly distributed around the opencast coal mining excavation areas, and the regions with the CoV of NDVI above 0.25 were mostly located in areas with low(28.8%) and medium-low(10.2%) vegetation cover.The average disturbance distances of mining activities on vegetation in the three mine groups(west, southwest and east) were 800, 800 and 1000 m, respectively.The greater the scale of mining, the farther the disturbance distances of mining activities on vegetation.We conclude that vegetation reclamation will certainly compensate for the negative impacts of opencast coal mining activities on vegetation.Sufficient attention should be paid to the proportional allocation of plant species(herbs and shrubs) in the reclamation areas, and the restored vegetation in these areas needs to be protected for more than 6 a.Then, as the repair time increased, the vegetation condition of the reclamation areas would exceed that of the natural areas.

Machine Learning-Based Seismic Fragility Analysis of Large-Scale Steel Buckling Restrained Brace Frames

Steel frames equipped with buckling restrained braces(BRBs)have been increasingly applied in earthquake-prone areas given their excellent capacity for resisting lateral forces.Therefore,special attention has been paid to the seismic risk assessment(SRA)of such structures,e.g.,seismic fragility analysis.Conventional approaches,e.g.,nonlinear finite element simulation(NFES),are computationally inefficient for SRA analysis particularly for large-scale steel BRB frame structures.In this study,amachine learning(ML)-based seismic fragility analysis framework is established to effectively assess the risk to structures under seismic loading conditions.An optimal artificial neural network model can be trained using calculated damage and intensity measures,a technique which will be used to compute the fragility curves of a steel BRB frame instead of employing NFES.Numerical results show that a highly efficient instantaneous failure probability assessment can be made with the proposed framework for realistic large-scale building structures.

Operating speed models for curved segments of highways in plateau regions

To examine the influence of the harsh environment in plateau areas on the operating speed of vehicles,advanced speed prediction models for curved segments are established based on observed actual speed data.First,the speed characteristics at the starting,mid,and end points of a plane curve were observed on Lalin Highway and China National Highway 318 with Bushnell s handheld radar speedometer 10-1911CN.Second,the stepwise regression method was proposed to determine the significant parameters and propose the prediction models of the operating speed of cars and large vehicles for the two highways.Finally,reserved test group data were utilized to prove the validity and practicality of the proposed models.Compared with traditional methods,the established models can produce more accurate prediction results and deeply examine the nonlinear relationships between parameters and the predicted operating speed.This study provides a considerate direction and basis for the operating speed prediction model for other segments in plateau regions.

Accurate estimation of soil shear strength parameters

The cost and safety of geotechnical engineering are highly depending on the accuracy of soil shear strength parameters.There are three methods often used to estimate soil shear strength parameters,i.e.,moment method,3-sigma rule and linear regression method.In this study,the accuracy of these three methods is compared.Traditional linear regression method(LRM)can only offer the mean of shear strength parameters.Some engineers misuse the standard error of shear strength indexes as the standard deviations.Such misuse may highly underestimate the uncertainty and induce high risk to the geotechnical design.A modified LRM is proposed to determine both the mean and variance of shear strength parameters.The moment method,three-sigma rule and LRM are used to analyze the tri-axial test data in Xiaolangdi Hydraulic Project and three numerical shear strength tests.The results demonstrate that:1)The modified LRM can offer the most accurate estimation to shear strength parameters;2)A dimensionless formula is much preferred in LRM rather than a dimensional formula.The stress ratio formula is much better than stress relation in the shear strength parameter analysis.The proposed method is applicable to shear strength parameter analysis for tri-axial test data,direct shear test and the un-drained shear strength test of stratified clay.

Climate change characteristics of Amur River

Unusually severe weather is occurring more frequently due to global climate change. Heat waves, rainstorms, snowstorms, and droughts are becoming increasingly common all over the world, threatening human lives and property. Both temperature and precipitation are representative variables usually used to directly reflect and forecast the influences of climate change. In this study, daily data （from 1953 to 1995） and monthly data （from 1950 to 2010） of temperature and precipitation in five regions of the Amur River were examined. The significance of changes in temperature and precipitation was tested using the Mann-Kendall test method. The amplitudes were computed using the linear least-squares regression model, and the extreme temperature and precipitation were analyzed using hydrological statistical methods. The results show the following： the mean annual temperature increased significantly from 1950 to 2010 in the five regions, mainly due to the warming in spring and winter; the annual precipitation changed significantly from 1950 to 2010 only in the lower mainstream of the Amur River; the frequency of extremely low temperature events decreased from 1953 to 1995 in the mainstream of the Amur River; the frequency of high temperature events increased from 1953 to 1995 in the mainstream of the Amur River; and the frequency of extreme precipitation events did not change significantly from 1953 to 1995 in the mainstream of the Amur River. This study provides a valuable theoretical basis for settling disputes between China and Russia on sustainable development and utilization of water resources of the Amur River.

Realization Path for Inclusive Finance to Support Rural Revitalization in Poverty-stricken Areas

Based on the quarterly economic,social and financial development data of 39 poverty-stricken counties in Henan Province during 2016-2018,this paper utilized the entropy-based TOPSIS method to objectively measure the rural revitalization index,and then built the quantile regression model to study the effects of various elements of inclusive finance on different stages of rural revitalization.Research results show that industrial development,agricultural modernization,targeted poverty alleviation,endogenous demand,and rural governance are the main points of inclusive finance in poverty-stricken areas to support rural revitalization;the rural revitalization index indicates that compared with the Dabie Mountain area and the non-contiguous poverty-stricken areas,the rural revitalization of the Qinba Mountain area is slower;for inclusive finance supporting rural revitalization,it is necessary to bring into play the role of monetary policy tools in re-lending,functions of credit in supporting industrial development,and role of insurance in risk protection;furthermore,inclusive finance solves problems such as the diminishing marginal effect of physical machinery investment in rural revitalization support,financial support for the coordinated development of small farmers and new agricultural business entities,financial support for the development of the entire industry chain,and the"siphon effect"of capital.

Study on comparison between absolute and relative input energy spectra and effects of ductility factor

Based on 266 strong ground motion records, an attenuation relationship was developed for both absolute and relative input energy spectra. The comparison of the two kinds of input energy spectra constructed from the attenuation relationship was made in this paper. The results show that there is little difference between the absolute input energy spectra and relative input energy spectra at the periods of 0.5-1.0 s for elastic systems and at the period of 0.5 s for inelastic systems. The absolute input energy spectra are much larger than relative input energy spectra in very short period range but some less than relative input energy spectra in long period range. It is also found that the ductility factor has a significant effect on both absolute and relative input energy spectra. The absolute input energy spectra increase with the increasing of ductility factor in the period range of less than 0.3 s but decrease in the period range of larger than 0.3 s. The absolute input energy spectra for different ductility factor are almost equivalent at the period about 0.3 s, but for relative input energy spectra, the period is about 0.5 s. The effect of ductility on the relative input energy spectra in the short period range is much larger than that on the absolute input energy spectra, especially on the softer site class.

Preparation of Organic Montmorillonite and Mechanical Properties of Montmorillonite/Unsaturated Polyester Composites

The synthesis process of organic montmorillonite was designed and some kinds of montmorillonitel unsaturated polyester composites using different interlayer spacing montmorillonite were prepared.The interlayer spacing of montmorillonite was investigated by XRD and was increased to 3.98nm.The relationship between the four influential factors and the interayer spacing were regressively analyzed and the mathematical model was established.and the result shows when the content of organic reagent was less than 70%,the relationship between the interlayer spacing and the content was linear as follotes;interlayer spacing=1.771＋2.828× concentration,the effect of the other factors was not significant.Additionally,the testing of mechanical properties of the composites showed the impact strength was improved by 217%.and the bending strength was improved by 355%,when using the montmorillonite（MMT）of the largest interlayer spacing（3.98nm）.The results of ESEM shows the interface is bonded well when the composite specimen contains the 3.98nm MMT.