Ecosystem service values of gardens in the Yellow River Basin,China

Studies on the ecosystem service value(ESV)of gardens are critical for informing evidence-based land management practices based on an understanding of the local ecosystem.By analyzing equivalent value factors(EVFs),this paper evaluated the values of 11 ecosystem services of gardens in the Yellow River Basin of China in 2019.High-precision land use survey data were used to improve the accuracy of the land use classification,garden areas,and spatial distribution of the ESVs of gardens.The results showed that garden ecosystem generally had high ESVs,especially in terms of the ESV of food production,which is worthy of further research and application to the practice of land use planning and management.Specifically,the value of one standard EVF of ecosystem services in 2019 was 3587.04 CNY/(hm^(2)·a),and the ESV of food production of gardens was much higher than that of croplands.Garden ecosystem provided an ESV of 1348.66×10^(8)CNY/a in the Yellow River Basin.The areas with the most concentrated ESVs of gardens were located in four regions:downstream in the Shandong-Henan zone along the Yellow River,mid-stream in the Shanxi-Shaanxi zone along the Yellow River,the Weihe River Basin,and upstream in the Qinghai-Gansu-Ningxia-Inner Mongolia zone along the Yellow River.The spatial correlation of the ESVs in the basin was significant(global spatial autocorrelation index Moran's I=0.464),which implied that the characteristics of high ESVs adjacent to high ESVs and low ESVs adjacent to low ESVs are prominent.In the Yellow River Basin,the contribution of the ESVs of gardens to the local environment and economy varied across regions.We also put forward some suggestions for promoting the construction of ecological civilization in the Yellow River Basin.The findings of this study provide important contributions to the research of ecosystem service evaluation in the Yellow River Basin.

DIAGNOSTIC VALUE OF SERUM INSULIN- LIKE GROWTH FACTOR BINDING PROTEIN- 3 IN CHILDREN WITH OR WITHOUT GROWTH HORMONE DEFICIENCY

OBJECTIVE: To study the value of serum insulin-like growth factor binding protein-3 (IGFBP-3) levels in differential diagnosis of growth hormone deficiency (GHD). METHODS: To measure serum IGFBP-3 levels by RIA in normal children and adolescents, GHD children and short-stature children without GHD. RESULTS: Serum level of IGFBP-3 in 129 children with untreated GHD and with no pubertal development was 1.6 +/- 0.9 mg/L, which was less than that in normal group of the same age, but overlapped with the normal children in Tanner stage I. After six-month treatment with recombinant human growth hormone (rhGH), serum level of IGFBP-3 in 59 GHD significantly increased from 1.3 +/- 0.7 mg/L to 2.7 +/- 0.9 mg/L, accompanied by an increase of body heights, growth velocities and serum level of IGF-1. Serum level of IGFBP-3 in 55 short-stature children without GHD was 3.3 +/- 2.2 mg/L, which was not significantly different from that in normal group. CONCLUSION: Serum IGFBP-3 level can reflect the status of GH secretion in children with GHD and is a useful marker for differential diagnosis of GHD.

Factor value determination and applicability evaluation of universal soil loss equation in granite gneiss region

Six types of runoff plots were set up and an experimental study was carried out to examine natural rate of soil and water loss in the granite gneiss region of northern Jiangsu Province in China. Through correlation analysis of runoff and soil loss during 364 rainfall events, a simplified and convenient mathematical formula suitable for calculating the rainfall erosivity factor （R） for the local region was established. Other factors of the universal soil loss equation （USLE model） were also determined. Relative error analysis of the soil loss of various plots calculated by the USLE model on the basis of the observed values showed that the relative error ranged from -3.5% to 9.9% and the confidence level was more than 90%. In addition, the relative error was 5.64% for the terraced field and 12.36% for the sloping field in the practical application. Thus, the confidence level was above 87.64%. These results provide a scientific basis for forecasting and monitoring soil and water loss, for comprehensive management of small watersheds, and for soil and water conservation planning in the region.

Evaluation of Yarn Quality and Optimization of Cotton Spinning Parameter Based on Factors Analysis

In this paper, the spinning parameters are optimized by using the method of factor analysis. The yarns obtained from four different spinning parameters are evaluated by this method. Two common factors, fineness unevenness and tenacity level, are extracted from the seven yarn-quality indexes. The accumulative contribution percentage of the two factors is up to 91.813%,and much information in the yarn-quality indexes is reflected by the two factors. Then the score of each factor is calculated to evaluate the quality of yarn. Based on that, the techniques are optimized. The result is well in line with spinning practices, so it is testified feasibly to use this method to optimize spinning parameter.

New Factor to Characterize Mechanism of “Strong Column-Weak Beam” of RC Frame Structures

Most reinforced concrete(RC)frame structures did not achieve the "strong column-weak beam" failure mode in recent big earthquakes, resulting in a large number of casualties and significant property loss. To deal with this serious problem, a new column-beam relative factor was proposed to characterize the relative yield situation of column ends and beam ends. By limiting the column-beam relative factor, RC frame structures could achieve the "strong column-weak beam" failure mode under the excitation of strong ground motions. The limit values of column-beam relative factor were calculated, analyzed and verified by using structural simulation models for corner columns in the bottom story of structures, which are destroyed most seriously in earthquakes. The results show that the limit values should be analyzed under bi-directional ground motion and with different axial compression ratios of columns. The peak ground acceleration(PGA)of ground motions has no significant effect on the limit values, while the type of strong ground motions has a significant effect on the limit values.

Study on the Inelastic Attenuation Coefficient,Site Response and Source Parameters in the Southeast Area of Gansu Province

Using digital seismograms recorded by the Gansu digital seismic network, the inelastic attenuation coefficient is calculated based on a genetic algorithm and the method proposed by Atkinson. Then, the site response and source parameters are investigated by the Moya method. The inversion results indicate the frequency-dependent inelastic attenuation, Q value, in the southeastern Gansu is estimated as Q （f） ： 404.2f^0.264 . Except for the Tianshui station, the site responses of the other stations do not show significant amplifications, which is consistent with their basement on rocks. The stress drops of all 39 earthquakes range between 1 × 10^5 and 7 × 10^6 Pa. We also found the dependence of corner frequency on seismic moment and seismic magnitude.

Altitudinal effect of soil n-alkane δD values on the eastern Tibetan Plateau and their increasing isotopic fractionation with altitude

Stable isotope paleoaltimetry has provided unprecedented insights into the topographic histories of many of the world's highest mountain ranges. However, on the Tibetan Plateau(TP), stable isotopes from paleosols generally yield much higher paleoaltitudes than those based on fossils. It is therefore essential when attempting to interpret accurately this region's paleoaltitudes that the empirical calibrations of local stable isotopes and the relations between them are established. Additionally,it is vital that careful estimations be made when estimate how different isotopes sourced from different areas may have been influenced by different controls. We present here 29 hydrogen isotopic values for leaf wax-derived n-alkanes(i.e., δD_(wax) values,and abundance-weighted average δD values of C_(29) and C_(31)) in surface soils, as well as the δD values of soil water(δD_(sw)) samples(totaling 22) from Mount Longmen(LM), on the eastern TP(altitude ~0.8–4.0 km above sea level(asl), a region climatically affected by the East Asian Monsoon(EAM). We compared our results with published data from Mount Gongga(GG). In addition,47 river water samples, 55 spring water samples, and the daily and monthly summer precipitation records(from May to October,2015) from two precipitation observation stations were collected along the GG transect for δD analysis. LM soil δD_(wax) values showed regional differences and responded strongly to altitude, varying from.160‰ to.219‰, with an altitudinal lapse rate(ALR) of.18‰ km^(-1)(R^2=0.83; p<0.0001; n=29). These δD_(wax) values appeared more enriched than those from the GG transect by ~40‰. We found that both the climate and moisture sources led to the differences observed in soil δD_(wax) values between the LM and GG transects. We found that, as a general rule, ε_(wax/rw), ε_(wax/p) and εwax/sw values(i.e., the isotopic fractionation of δD_(wax) corresponding to δD_(rw), δD_p and δD_(sw)) increased with increasing altitude along both the LM and GG transects(up to 34‰ and 50‰, respectively). Basing its research on a comparative study of δD_(wax), δD_p, δD_(rw)(δD_(springw)) and δD_(sw), this paper discusses the effects of moisture recycling, glacier-fed meltwater, relative humidity(RH), evapotranspiration(ET), vegetation cover, latitude,topography and/or other factors on ε_(wax/p) values. Clearly, if ε_(wax-p) values at higher altitudes are calculated using smaller ε_(wax-p) values from lower altitudes, the calculated paleowaterδD_p values are going to be more depleted than the actual δD values, and any paleoaltitude would therefore be overestimated.

A novel OFS–TLBO–SVR hybrid model for optimal budget allocation of government schemes to maximize GVA at factor cost

Support Vector Regression (SVR) has already been proved to be one of the mostreferred and used machine learning technique in various fields. In this study, wehave addressed a predictive-cum-prescriptive analysis for finalizing fundallocations by the Government at center to the schemes under Central Plan andto the schemes under States and Union Territories Plan, with a goal to maximizeGross Value Added (GVA) at factor cost. Here, we have proposed a hybridmachine learning model comprising of OFS (Orthogonal Forward Selection),TLBO (Teaching Learning Based Optimization) and SVR for the prediction ofGVA at factor cost. In this model, referred as OFS–TLBO–SVR hybrid model,SVR is at the core of prediction mechanism, OFS is for identifying the relevantfeatures, and TLBO is to support in optimizing the free parameters of SVR andagain TLBO is used for optimizing the governable attributes of data.