Discussion on Statistical Monitoring and Evaluation of High-Quality Development of County Economy

The county economy serves as the fundamental unit in China’s economic development and construction.There exists a significant correlation between the quality and level of county economic development and the overall economic construction level of the country.In recent years,China’s economy has experienced rapid growth,contributing to an improved living environment and substantial economic income for its citizens.However,this progress has also brought to light certain issues,such as an unbalanced industrial structure and inefficient resource utilization,leading to problems such as low efficiency and severe environmental pollution.Therefore,it becomes imperative to enhance the statistical monitoring and evaluation of high-quality county economic development.This approach aims to gather insights into the development status of the county economy and provide essential data support for decision-making in county economic development.Consequently,this paper proposes development suggestions for the statistical monitoring and evaluation of high-quality county economic development,serving as a crucial starting point for future initiatives.

Evaluation of the Relationship between Bacteria Concentration and the Strength and Durability of Self-compacting Concrete Incorporating Sporosarcina pasteurii

This research was carried out to evaluate the relationship between the incorporation of calcite precipitation bacteria,sporosarcina pasteurii using calcium lactate as nutrient source and the properties of calcined clay and limestone powder blended self-compacting concrete.Ten mixes were designed and designated S0 to S9 with S0 the control(without bacteria and nutrient)and S1 to S9 at varying bacteria and calcium lactate concentrations and the effect of the bacteria cell density and calcium lactate concentration on the compressive strength,sorptivity and tensile strength with age were evaluated using experimental program and statistical packages(ANOVA and post hoc tests).The result of both the experimental program and statistical evaluation shows that the incorporation of sporosarcina pasteurii and calcium lactate as nutrients had a positive impact on the properties of the ternary blended self-compacting concrete.

A Hybrid Statistical-Dynamical Downscaling of Air Temperature over Scandinavia Using the WRF Model

An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications.In this work,a hybrid statistical–dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean,minimum and maximum air temperatures to investigate the quality of localscale estimates produced by downscaling.These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute over a near-coastal region of western Finland.The dynamical downscaling is performed with the Weather Research and Forecasting(WRF)model,and the statistical downscaling method implemented is the Cumulative Distribution Function-transform(CDF-t).The CDF-t is trained using 20 years of WRF-downscaled Climate Forecast System Reanalysis data over the region at a 3-km spatial resolution for the central month of each season.The performance of the two methods is assessed qualitatively,by inspection of quantile-quantile plots,and quantitatively,through the Cramer-von Mises,mean absolute error,and root-mean-square error diagnostics.The hybrid approach is found to provide significantly more skillful forecasts of the observed daily mean and maximum air temperatures than those of the dynamical-only downscaling(for all seasons).The hybrid method proves to be less computationally expensive,and also to give more skillful temperature forecasts(at least for the Finnish near-coastal region).

Predicting the alloying element yield in a ladle furnace using principal component analysis and deep neural network

The composition control of molten steel is one of the main functions in the ladle furnace(LF)refining process.In this study,a feasible model was established to predict the alloying element yield using principal component analysis(PCA)and deep neural network(DNN).The PCA was used to eliminate collinearity and reduce the dimension of the input variables,and then the data processed by PCA were used to establish the DNN model.The prediction hit ratios for the Si element yield in the error ranges of±1%,±3%,and±5%are 54.0%,93.8%,and98.8%,respectively,whereas those of the Mn element yield in the error ranges of±1%,±2%,and±3%are 77.0%,96.3%,and 99.5%,respectively,in the PCA-DNN model.The results demonstrate that the PCA-DNN model performs better than the known models,such as the reference heat method,multiple linear regression,modified backpropagation,and DNN model.Meanwhile,the accurate prediction of the alloying element yield can greatly contribute to realizing a“narrow window”control of composition in molten steel.The construction of the prediction model for the element yield can also provide a reference for the development of an alloying control model in LF intelligent refining in the modern iron and steel industry.

A new nonlinear empirical strength criterion for rocks under conventional triaxial compression

The failure criterion of rocks is a critical factor involved in reliability design and stability analysis of geotechnical engineering.In order to accurately evaluate the triaxial compressive strength of rocks under different confining pressures,a nonlinear empirical strength criterion based on Mohr-Coulomb criterion was proposed in this paper.Through the analysis of triaxial test strength of 11 types of rock materials,the feasibility and validity of proposed criterion was discussed.For a further verification,six typical strength criteria were selected,and the prediction results of each criterion and test results were statistically analyzed.The comparative comparison results show that the prediction results obtained by applying this new criterion to 97 conventional triaxial compression tests of 11 different rock materials are highly consistent with the experimental data.Statistical analysis was executed to assess the application of the new criterion and other classical criteria in predicting the failure behavior of rock.This proposed empirical criterion provides a new reference and method for the determination of triaxial compressive strength of rock materials.

Proposals of SPT-CPT and DPL-CPT correlations for sandy soils in Brazil

Field tests in geotechnical engineering are fundamental for identification of the underground conditions.The standard penetration test(SPT) is the most commonly used geotechnical approach. There has been an increase both in the use and application of the in situ tests: cone penetration test(CPT) and dynamic probing(DP). Several empirical SPT-CPT and dynamic probing light(DPL)-CPT correlations for sandy soils have been discussed in the literature. New SPT-CPT and DPL-CPT correlations for the sandy soils of the city of Vitoria, in the southeast of Brazil, are suggested in this paper. Statistical analyses to evaluate the quality of the data used are performed, and the suggested correlations are validated with several previous published datasets. The paper also provides some insights into SPT-CPT correlations and soil characteristics(i.e. the mean particle size and the fines fraction of the soil).

MULTI-BLOCK CHAINING-BASED AUTHENTICATION MODE

A fast authentication mode based on Multi-Block Chaining (MBC) is put forward; and its security is proved. The MBC mode is for new generation block cipher algorithms. Its speed is about 13% faster than that of the authentication modes in common use (for example, cipher block chaining-message authentication code mode). The dependence test results meet the requirement. The MBC mode is complete; its degree of ava-lanche effect is about 0.9993; its degree of strict avalanche criterion is 0.992 or so. The frequency test results indicate that the output generated by the MBC mode has uniformity. The binary matrix rank test results imply that it is linear independent among disjoint sub-matrices of the output. Maurer’s universal statistical test results show that the output could be significantly compressed without loss of information. Run test, spectral test, non-overlapping template matching test, overlapping template matching test, Lempel-Ziv compression test, linear complexity test, serial test, approximate entropy test, cumulative sums test, random excursions test and random excursions variant test results fulfill the requirements of all. Therefore the MBC mode has good pseudo-randomness. Thus the security of MBC mode is verified by the way of statistical evaluation.

Efficacy and Challenges of Using Springs for Early Detection of Contaminant Release from Waste Disposal Facilities Constructed in Karst Terranes

Early detection of groundwater contamination from waste disposal facilities is challenging in karst terranes. First, one needs to demonstrate that the groundwater system at the study site is monitorable. Both springs and wells are potential monitoring locations if they are effectively connected to the groundwater system, and they are not impacted by any other disposal facilities. Second, due to dynamic responses to recharge events, particularly discharge and chemical constituents at karst springs, multiple-parameter, long-term, and high-frequency monitoring may be required to collect background data. Sampling and analysis plans should be designed to reflect the unique characteristics of the monitoring locations. Characterization of the natural variations in water quality may require sampling efforts under different flow conditions. Third, evaluation of the potential impact of waste disposal units on the groundwater system requires an effective statistical evaluation program. Due to heterogeneity of karst aquifers, intra-locational comparison is generally preferred to inter-locational comparison. Sufficient groundwater monitoring data prior to construction of waste disposal units are required to develop the intra-locational statistical evaluation. In the case study presented in this paper, procedures to address these above-mentioned challenges were presented for two springs using seven dye tracing tests, two spring instrumentations, nine background sampling, flow-weighted concentrations, and an innovative statistical evaluation method were presented. These procedures were developed to evaluate potential contaminant release from a solid waste disposal facility constructed in a relatively isolated karst terrane. Although the specific procedures may not be duplicated, the overall technical approaches discussed in the paper may shed light on groundwater monitoring programs in other karst areas.

The Impact of Discovery Education Digital Textbook Usage on Students＇ Achievements

Discovery Education digital textbook series help accelerating school districts＇ digital transition in different subjects for K-8 and high school classrooms and related services. We evaluated the impact of using Discovery Education digital textbooks on students＇ science, english and math achievement in a large school district in southwest United States.

Increase of Local Seismicity Due to the Construction of Taipei 101?

Considering the controversy over whether the Taipei 101 skyscraper has induced an increase of local seismicity, we reanalyzed the local earthquake catalogue before and after the construction to investigate whether the reported seismicity increase related to the construction is statistically significant. Statistical tests obtained a positive conclusion about the apparent seismicity increase. However, the calculation of maximum ACFS shows that at most a 2.2 × 10^-4 bar Coulomb Failure Stress change was produced by the construction. Therefore, whether the construction of Taipei 101 really triggered the increase of seismicity still remains an open question： Statistically-yes; physically-no.

Deterministic tools to predict gas assisted gravity drainage recovery factor

Naturally fractured rocks contain most of the world's petroleum reserves.This significant amount of oil can be recovered efficiently by gas assisted gravity drainage(GAGD).Although,GAGD is known as one of the most effective recovery methods in reservoir engineering,the lack of available simulation and mathematical models is considerable in these kinds of reservoirs.The main goal of this study is to provide efficient and accurate methods for predicting the GAGD recovery factor using data driven techniques.The proposed models are developed to relate GAGD recovery factor to the various parameters including model height,matrix porosity and permeability,fracture porosity and permeability,dip angle,viscosity and density of wet and non-wet phases,injection rate,and production time.In this investigation,by considering the effective parameters on GAGD recovery factor,three different efficient,smart,and fast models including artificial neural network(ANN),least square support vector machine(LSSVM),and multi-gene genetic programming(MGGP)are developed and compared in both fractured and homogenous porous media.Buckinghamπtheorem is also used to generate dimensionless numbers to reduce the number of input and output parameters.The efficiency of the proposed models is examined through statistical analysis of R-squared,RMSE,MSE,ARE,and AARE.Moreover,the performance of the generated MGGP correlation is compared to the traditional models.Results demonstrate that the ANN model predicts the GAGD recovery factor more accurately than the LSSVM and MGGP models.The maximum R^(2)of 0.9677 and minimum RMSE of 0.0520 values are obtained by the ANN model.Although the MGGP model has the lowest performance among the other used models(the R2 of 0.896 and the RMSE of 0.0846),the proposed MGGP correlation can predict the GAGD recovery factor in fractured and homogenous reservoirs with high accuracy and reliability compared to the traditional models.Results reveal that the employed models can easily predict GAGD recovery factor without requiring complicate governing equations or running complex and time-consuming simulation models.The approach of this research work improves our understanding about the most significant parameters on GAGD recovery and helps to optimize the stages of the process,and make appropriate economic decisions.

Efficiency of local minima and GLM techniques in sinkhole extraction from a LiDAR-based terrain model

The aim of this paper was to study reliable automated delineationpossibilities of karst sinkholes using a LiDAR-based digital terrain model(DTM) with pixel-based classifications. We applied two approaches toextract sinkholes: (1) general linear modeling (GLM) with morphometricindices derived from DTM;(2) and a local minima-based delineationusing only LiDAR DTM as the input layer. The outcome of the localminima was significantly different from the reference ones but found allthe sinkholes without previous knowledge of the area. The GLM-basedoutcome did not differ statistically from the reference. Results showedthat these approaches were efficient in detecting sinkholes based onLIDAR derivatives, and can be used for risk assessment and hazardpreparedness in karst areas: GLM had an overall accuracy of 89.5% andlocal minima had an accuracy of 92.3%;both methods identifiedsinkholes but also had commission errors, identifying depressions assinkholes.