Explanatory Notes on Main Statistical Indicators

Comparable Prices refer to prices that are used to remove the factors of price change in calculating economic aggregates, so as to facilitate comparison of aggregates over time. Two methods are used for calculating economic aggregates at comparable prices: 1. Multiplying the output of products by their constant prices of certain year; 2. Deflation of data at current prices by relevant price index.

Explanatory Notes on Main Statistical Indicators

Comparable Prices refer to prices that are used to remove the factors of price change in calculating economic aggregates,soas to facilitate comparison of aggregates over time.Two methods are used for calculating economic aggregates at comparableprices:1.Multiplying the output of products by their constant prices of certain year;2.Deflation of data at current prices byrelevant price index.Constant Price refers to the average price of a given product in certain year,which is used for comparison of output valueover time.As the output value at constant prices removes the factor of price changes,it reflects the trend of productiondevelopment over time.Since 1949,with the changes in general price level,National Bureau of Statistics has

Explanatory Notes on Main Statistical Indicators

Comparable Prices refer to prices that are used to remove the factors of price change in calculating economicaggregates, so as to facilitate comparison of aggregates over time. Two methods are used for calculating economicaggregates at comparable prices: 1. Multiplying the output of products by their constant prices of certain year;2. Deflation of data at current prices by relevant price index.

Innovation and Education： Construction of Interactivity Indicators to Collaborative and Immersive Learning

Technological innovation, driven towards the educational context, has favored the concept of interactive technological environments that may significantly contribute towards the teaching-learning processes. In that sense, mapping interactivity indicators that consider technical and operational aspects, supported by the available technical literature and based on the perspective of undergraduate engineers and teachers, becomes a fundamental activity in order to build interactive environments that may in fact adequately contribute for the professional education of our students, mainly the ones in engineering courses. Specifically, this paper shows preliminary studies within this perspective, showing how technological innovation may be understood and applied in the educational context. The study also shows the first interactivity indicator for a collaborative learning perspective, obtained from data collected through a qualitative content analysis methodology.

A Discussion on Possible Indicators Related to Genetic Structure Changes in Plant Germplasm Conservation

The purpose of the present paper is to study and develop indicators and procedures for the evaluation of genetic structure changes in germplasm conservation due to social and natural environment reasons. Some basic concepts in germplasm study were introduced at first. Then, six kinds of indicators for genetic diversity as a measure of genetic potential of a germplasm collection were presented, i.e., numbers of different entities at certain level, evenness of the entity distribution, genetic similarity and genetic distance, genetic variance and genetic coefficient of variation, multivariate genetic variation indices, and coefficient of parentage. It was pointed out that genetic dispersion did not provide a complete concept of genetic diversity if without any information from genetic richness. Based on the above, the indicators for genetic erosion as the genetic structure changes of germplasm conservation due to social reasons, the indicators of genetic vulnerability as the genetic structure changes of germplasm conservation due to environmental stresses, the measurement of genetic drift and genetic shift as the genetic structure changes of germplasm collection during reproduction or seed increase were reviewed and developed. Furthermore, the estimation procedures of the indicators by using molecular markers were suggested. Finally, the case studies on suitable conservation sample size of self-pollinated and open-pollinated populations were given for reference.

Prediction of the Diffuse Solar Energy on Horizontal at Different Selected Locations

The main objective of this paper is to predict the diffuse solar energy on a horizontal surface by using data of global solar energy (H) and diffuse solar energy (Hd) at different selected geographical locations in Saudi Arabia during the period time from 1980 to 2019. The low values of the root mean square error RMSE for all correlations indicated a good agreement between the measured and calculated values of Hd. The negative values of mean percentage error MPE % for all models show that for all locations, the proposed correlations slightly overestimate Hd, and the absolute values of MPE never reach 1.35%. The first, second and third order correlations between the diffuse solar fraction Hd/H and the clearness index Kt and between the diffuse transmittance Hd/H0 and the sunshine hours have been proposed for the selected locations using the method of regression analysis. The differences between the measured and calculated values of Hd show that a first order correlation between Hd/H and Kt can be used for estimating Hd at the present locations with good accuracy. However, second order correlations between Hd/H or Hd/H0 and S/So are recommended for estimating Hd at these locations. The average annual differences between measured and calculated values of diffuse solar energy Hd on horizontal at selected sites in the present research are discussed.

Empirical Models for the Evaluation of Global Solar Radiation for the Site of Abeche in the Province of Ouaddaï, in Chad

The global sustainability plan for future development relies on solar radiation which is the main source of renewable energy. Thus, this work studies the performance of six models to estimate global solar radiation on a horizontal surface for the Abeche site in Chad. The data used in this work were collected at the General Directorate of National Meteorology of Chad. The reliability and accuracy of different models for estimating global solar radiation were validated by statistical indicators to identify the most accurate model. The results show that among all the models, the Sabbagh model has the best performance in estimating the global solar radiation. The average is 6.354 kWh/m2 with an average of -3.704%. This model is validated against NASA data which is widely used.

Simulation of Daily Diffuse Solar Radiation Based on Three Machine Learning Models

Solar radiation is an important parameter in the fields of computer modeling,engineering technology and energy development.This paper evaluated the ability of three machine learning models,i.e.,Extreme Gradient Boosting(XGBoost),Support Vector Machine(SVM)and Multivariate Adaptive Regression Splines(MARS),to estimate the daily diffuse solar radiation(Rd).The regular meteorological data of 1966-2015 at five stations in China were taken as the input parameters(including mean average temperature(Ta),theoretical sunshine duration(N),actual sunshine duration(n),daily average air relative humidity(RH),and extra-terrestrial solar radiation(Ra)).And their estimation accuracies were subjected to comparative analysis.The three models were first trained using meteorological data from 1966 to 2000.Then,the 2001-2015 data was used to test the trained machine learning model.The results show that the XGBoost had better accuracy than the other two models in coefficient of determination(R2),root mean square error(RMSE),mean bias error(MBE)and normalized root mean square error(NRMSE).The MARS performed better in the training phase than the testing phase,but became less accurate in the testing phase,with the R2 value falling by 2.7-16.9%on average.By contrast,the R2 values of SVM and XGBoost increased by 2.9-12.2%and 1.9-14.3%,respectively.Despite trailing slightly behind the SVM at the Beijing station,the XGBoost showed good performance at the rest of the stations in the two phases.In the training phase,the accuracy growth is small but observable.In addition,the XGBoost had a slightly lower RMSE than the SVM,a signal of its edge in stability.Therefore,the three machine learning models can estimate the daily Rd based on local inputs and the XGBoost stands out for its excellent performance and stability.

Pedotransfer Functions for Estimating Soil Hydraulic Properties： A Review

Characterization of soil hydraulic properties is important to environment management; however, it is well recognized that it is laborious, time-consuming and expensive to directly measure soil hydraulic properties. This paper reviews the development of pedotransfer functions （PTFs） used as an alternative tool to estimate soil hydraulic properties during the last two decades. Modern soil survey techniques like satellite imagery/remote sensing has been used in developing PTFs. Compared to mechanistic approaches, empirical relationships between physical properties and hydraulic properties have received wide preference for predicting soil hydraulic properties. Many PTFs based on different parametric functions can be found in the literature. A number of researchers have pursued a universal function that can describe water retention characteristics of all types of soils, but no single function can be termed generic though van Cenuchten （VG） function has been the most widely adopted. Most of the reported parametric PTFs focus on estimation of VG parameters to obtain water retention curve （WRC）. A number of physical, morphological and chemical properties have been used as predictor variables in PTFs. Conventionally, regression algorithms/techniques （statistical/neurM regression） have been used for calibrating PTFs. However, there are reports of utilizing data mining techniques, e.g., pattern recognition and genetic algorithm. It is inferred that it is critical to refine the data used for calibration to improve the accuracy and reliability of the PTFs. Many statistical indices, including root mean square error （RMSE）, index of agreement （d）, maximum absolute error （ME）, mean absolute error （MAE）, coefficient of determination （r2） and correlation coefficient （r）, have been used by different researchers to evaluate and validate PTFs. It is argued that being location specific, research interest in PTFs will continue till generic PTFs are developed and validated. In future studies, improved methods will be required to extract information from the existing database.

Smart model for accurate estimation of solar radiation

Prediction of solar radiation has drawn increasing attention in the recent years.This is because of the lack of solar radiation measurement stations.In the present work,14 solar radiation models have been used to assess monthly global solar radiation on a horizontal surface as function of three parameters:extraterrestrial solar irradiance(),duration sunshine()and daylight hours().Since it has been observed that each model is adequate for some months of the year,one model cannot be used for the prediction of the whole year.Therefore,a smart hybrid system is proposed which selects,based on the intelligent rules,the most suitable prediction model of the 14 models listed in this study.For the test and evaluation of the proposed models,Tamanrasset city,which is located in the south of Algeria,is selected for this study.The meteorological data sets of five years(2000–2004)have been collected from the Algerian National Office of Meteorology(NOM),and two spatial databases.The results indicate that the new hybrid model is capable of predicting the monthly global solar radiation,which offers an excellent measuring accuracy of values ranging from 93%to 97%in this location.