Test paper evaluation is an important work for the management of tests,which results are significant bases for scientific summation of teaching and learning.Taking an English test paper of high students'monthly ex...Test paper evaluation is an important work for the management of tests,which results are significant bases for scientific summation of teaching and learning.Taking an English test paper of high students'monthly examination as the object,it focuses on the interpretation of SPSS output concerning item and whole quantitative analysis of papers.By analyzing and evaluating the papers,it can be a feedback for teachers to check the students'progress and adjust their teaching process.展开更多
Based on the field survey of the foxtail millet planting,processing and sales in foxtail millet production areas,it found out basic demands of foxtail millet farmers and processing enterprises for policies. It made st...Based on the field survey of the foxtail millet planting,processing and sales in foxtail millet production areas,it found out basic demands of foxtail millet farmers and processing enterprises for policies. It made statistical analysis with the aid of SPSS statistical software. It found that current situation of China's foxtail millet industry is not optimistic. Finally,it came up with policy recommendations including enhancing actual effect of policy,establishing special fund,increasing scientific and technological support,and encouraging mechanized planting.展开更多
Chinese traditional shadow play has been selected into the List of Intangible Cultural Heritage in 2011.Yet,reflecting abundant national cultural values,such traditional art form is degenerating and fading out from pe...Chinese traditional shadow play has been selected into the List of Intangible Cultural Heritage in 2011.Yet,reflecting abundant national cultural values,such traditional art form is degenerating and fading out from people’s sight.As the earliest statistical analysis software,Statistical Package for the Social Science(SPSS)is comprehensive in analyzing and managing statistical data.This study explores the application of SPSS in minimizing the workload of researchers while raising the validity of data in supporting the analysis of the survey data which reflected the inheritance and development of Chinese traditional shadow play in schools.展开更多
Through a questionnaire survey of primary and secondary school teachers and Normal Students to carry out a statistical analysis of the data based on SPSS 17.0 statistical analysis software. So we draw primary and seco...Through a questionnaire survey of primary and secondary school teachers and Normal Students to carry out a statistical analysis of the data based on SPSS 17.0 statistical analysis software. So we draw primary and secondary school teachers and normal students to arrive at the current status quo of educational technology ability and analyzes the strengths and weaknesses of the ability of its parts. Finally, we proposed new initiatives and methods of education and technical ability training.展开更多
Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,su...Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.展开更多
随着中国对教育数字化要求的不断提高,传统应用统计学教学模式已难以满足学生应用能力的培养需求。针对普通应用型高校应用统计学教学过程中存在的教学手段单一、教学内容滞后和教学评价落后等一系列问题,以社会科学统计软件(Statistica...随着中国对教育数字化要求的不断提高,传统应用统计学教学模式已难以满足学生应用能力的培养需求。针对普通应用型高校应用统计学教学过程中存在的教学手段单一、教学内容滞后和教学评价落后等一系列问题,以社会科学统计软件(Statistical Package for the Social Sciences,SPSS)教学为例,在基于成果导向教育(Outcome-Based Education,OBE)理念的指导下,从人工智能(Artificial Intelligence,AI)赋能课程设计、学生个性化自主学习、过程化考核和教学综合评价四方面改革教学模式。通过对AI赋能的数字化教学模式的探索与实践,切实提高了学生解决复杂统计学问题的实践能力,顺应了教育数字化时代对应用统计学专业人才的需求。展开更多
Coral reef limestone(CRL)constitutes a distinctive marine carbonate formation with complex mechanical properties.This study investigates the multiscale damage and fracture mechanisms of CRL through integrated experime...Coral reef limestone(CRL)constitutes a distinctive marine carbonate formation with complex mechanical properties.This study investigates the multiscale damage and fracture mechanisms of CRL through integrated experimental testing,digital core technology,and theoretical modelling.Two CRL types with contrasting mesostructures were characterized across three scales.Macroscopically,CRL-I and CRL-II exhibited mean compressive strengths of 8.46 and 5.17 MPa,respectively.Mesoscopically,CRL-I featured small-scale highly interconnected pores,whilst CRL-II developed larger stratified pores with diminished connectivity.Microscopically,both CRL matrices demonstrated remarkable similarity in mineral composition and mechanical properties.A novel voxel average-based digital core scaling methodology was developed to facilitate numerical simulation of cross-scale damage processes,revealing network-progressive failure in CRL-I versus directional-brittle failure in CRL-II.Furthermore,a damage statistical constitutive model based on digital core technology and mesoscopic homogenisation theory established quantitative relationships between microelement strength distribution and macroscopic mechanical behavior.These findings illuminate the fundamental mechanisms through which mesoscopic structure governs the macroscopic mechanical properties of CRL.展开更多
本科招生数据的挖掘与录取趋势分析在教育领域中具有重要应用价值。研究利用社会科学统计软件包(Statistical Package for the Social Sciences,SPSS)工具对本科招生数据进行处理与分析,借助清洗并标准化数据集与筛选相关变量,探索数据...本科招生数据的挖掘与录取趋势分析在教育领域中具有重要应用价值。研究利用社会科学统计软件包(Statistical Package for the Social Sciences,SPSS)工具对本科招生数据进行处理与分析,借助清洗并标准化数据集与筛选相关变量,探索数据背后的规律与趋势。基于时间序列分析与数据挖掘模型来揭示招生过程中的规律性变化,分析不同因素对录取结果的影响机制,借助建立预测模型对未来的招生趋势进行有效预测,并验证模型的准确性与可靠性。数据挖掘方法在分析招生趋势和优化招生决策中具有较大潜力,为高等教育管理提供参考依据。展开更多
文摘Test paper evaluation is an important work for the management of tests,which results are significant bases for scientific summation of teaching and learning.Taking an English test paper of high students'monthly examination as the object,it focuses on the interpretation of SPSS output concerning item and whole quantitative analysis of papers.By analyzing and evaluating the papers,it can be a feedback for teachers to check the students'progress and adjust their teaching process.
基金Supported by Special Project for Construction of Modern Agricultural Industrial Technology System of the Ministry of Agriculture and Ministry of Finance(CARS-07-12.5-A18)
文摘Based on the field survey of the foxtail millet planting,processing and sales in foxtail millet production areas,it found out basic demands of foxtail millet farmers and processing enterprises for policies. It made statistical analysis with the aid of SPSS statistical software. It found that current situation of China's foxtail millet industry is not optimistic. Finally,it came up with policy recommendations including enhancing actual effect of policy,establishing special fund,increasing scientific and technological support,and encouraging mechanized planting.
文摘Chinese traditional shadow play has been selected into the List of Intangible Cultural Heritage in 2011.Yet,reflecting abundant national cultural values,such traditional art form is degenerating and fading out from people’s sight.As the earliest statistical analysis software,Statistical Package for the Social Science(SPSS)is comprehensive in analyzing and managing statistical data.This study explores the application of SPSS in minimizing the workload of researchers while raising the validity of data in supporting the analysis of the survey data which reflected the inheritance and development of Chinese traditional shadow play in schools.
文摘Through a questionnaire survey of primary and secondary school teachers and Normal Students to carry out a statistical analysis of the data based on SPSS 17.0 statistical analysis software. So we draw primary and secondary school teachers and normal students to arrive at the current status quo of educational technology ability and analyzes the strengths and weaknesses of the ability of its parts. Finally, we proposed new initiatives and methods of education and technical ability training.
基金funded through India Meteorological Department,New Delhi,India under the Forecasting Agricultural output using Space,Agrometeorol ogy and Land based observations(FASAL)project and fund number:No.ASC/FASAL/KT-11/01/HQ-2010.
文摘Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.
文摘随着中国对教育数字化要求的不断提高,传统应用统计学教学模式已难以满足学生应用能力的培养需求。针对普通应用型高校应用统计学教学过程中存在的教学手段单一、教学内容滞后和教学评价落后等一系列问题,以社会科学统计软件(Statistical Package for the Social Sciences,SPSS)教学为例,在基于成果导向教育(Outcome-Based Education,OBE)理念的指导下,从人工智能(Artificial Intelligence,AI)赋能课程设计、学生个性化自主学习、过程化考核和教学综合评价四方面改革教学模式。通过对AI赋能的数字化教学模式的探索与实践,切实提高了学生解决复杂统计学问题的实践能力,顺应了教育数字化时代对应用统计学专业人才的需求。
基金National Key Research and Development Program of China (No.2021YFC3100800)the National Natural Science Foundation of China (Nos.42407235 and 42271026)+1 种基金the Project of Sanya Yazhou Bay Science and Technology City (No.SCKJ-JYRC-2023-54)supported by the Hefei advanced computing center
文摘Coral reef limestone(CRL)constitutes a distinctive marine carbonate formation with complex mechanical properties.This study investigates the multiscale damage and fracture mechanisms of CRL through integrated experimental testing,digital core technology,and theoretical modelling.Two CRL types with contrasting mesostructures were characterized across three scales.Macroscopically,CRL-I and CRL-II exhibited mean compressive strengths of 8.46 and 5.17 MPa,respectively.Mesoscopically,CRL-I featured small-scale highly interconnected pores,whilst CRL-II developed larger stratified pores with diminished connectivity.Microscopically,both CRL matrices demonstrated remarkable similarity in mineral composition and mechanical properties.A novel voxel average-based digital core scaling methodology was developed to facilitate numerical simulation of cross-scale damage processes,revealing network-progressive failure in CRL-I versus directional-brittle failure in CRL-II.Furthermore,a damage statistical constitutive model based on digital core technology and mesoscopic homogenisation theory established quantitative relationships between microelement strength distribution and macroscopic mechanical behavior.These findings illuminate the fundamental mechanisms through which mesoscopic structure governs the macroscopic mechanical properties of CRL.
文摘本科招生数据的挖掘与录取趋势分析在教育领域中具有重要应用价值。研究利用社会科学统计软件包(Statistical Package for the Social Sciences,SPSS)工具对本科招生数据进行处理与分析,借助清洗并标准化数据集与筛选相关变量,探索数据背后的规律与趋势。基于时间序列分析与数据挖掘模型来揭示招生过程中的规律性变化,分析不同因素对录取结果的影响机制,借助建立预测模型对未来的招生趋势进行有效预测,并验证模型的准确性与可靠性。数据挖掘方法在分析招生趋势和优化招生决策中具有较大潜力,为高等教育管理提供参考依据。
