In this study we measured the △P(initial speed of gas emission) index with different gas concentrations of carbon dioxide(pure CO2,90% CO2+10% CH4,67% CO2+33% CH4,50% CO2+50% CH4,30% CO2+10% CH4 and pure CH4) of coal...In this study we measured the △P(initial speed of gas emission) index with different gas concentrations of carbon dioxide(pure CO2,90% CO2+10% CH4,67% CO2+33% CH4,50% CO2+50% CH4,30% CO2+10% CH4 and pure CH4) of coal samples from the No.2 coal seam in the Yaojie Coal Mine,Gansu province,China.The effect of carbon dioxide concentration,gas composition,coal strength and particle size of coal samples on the △P index was investigated.The experimental results show that with gas of various compositions,the △P value of three samples were clearly different.The △P index of coal samples A,B and C(0.2～0.25 mm) were 4,6 and 7 with pure CH4 and 22,30 and 21 when pure CH4 was used.Carbon dioxide concentration affects the △P index markedly.The △P index increases with an increase in carbon dioxide concentration,especially for coal B.Hence,the △P index and K(another outburst index) values tested only with pure CH4 for prediction of the danger of outburst is not accurate.It is important to determine the initial speed of gas emission given the gas composition of the coal seam to be tested for exact outburst prediction.展开更多
Phosphorus (P) risk indices are commonly used in the USA to estimate the field-scale risk of agricultural P runoff. Because the Ohio P Risk Index is increasingly being used to judge farmer performance, it is important...Phosphorus (P) risk indices are commonly used in the USA to estimate the field-scale risk of agricultural P runoff. Because the Ohio P Risk Index is increasingly being used to judge farmer performance, it is important to evaluate weighting/scoring of all P Index parameters to ensure Ohio farmers are credited for practices that reduce P runoff risk and not unduly penalized for things not demonstrably related to runoff risk. A sensitivity analysis provides information as to how sensitive the P Index score is to changes in inputs. The objectives were to determine 1) which inputs are most highly associated with P Index scores and 2) the relative impact of each input variable on resultant P Index scores. The current approach uses simulations across 6134 Ohio point locations and five crop management scenarios (CMSs), representing increasing soil disturbance. The CMSs range from all no-till, which is being promoted in Ohio, rotational tillage, which is a common practice in Ohio to full tillage to represent an extreme practice. Results showed that P Index scores were best explained by soil test P (31.9%) followed by connectivity to water (29.7%), soil erosion (13.4%), fertilizer application amount (11.3%), runoff class (9.5%), fertilizer application method (2.2%), and finally filter strip (2.0%). Ohio P Index simulations across CMSs one through five showed that >40% scored <15 points (low) while <1.5% scored >45 points (very high). Given Ohio water quality problems, the Ohio P Index needs to be stricter. The current approach is useful for Ohio P Index evaluations and revision decisions by spatially illustrating the impact of potential changes regionally and state-wide.展开更多
Forecasting the movement of stock market is a long-time attractive topic. This paper implements different statistical learning models to predict the movement of S&P 500 index. The S&P 500 index is influenced b...Forecasting the movement of stock market is a long-time attractive topic. This paper implements different statistical learning models to predict the movement of S&P 500 index. The S&P 500 index is influenced by other important financial indexes across the world such as commodity price and financial technical indicators. This paper systematically investigated four supervised learning models, including Logistic Regression, Gaussian Discriminant Analysis (GDA), Naive Bayes and Support Vector Machine (SVM) in the forecast of S&P 500 index. After several experiments of optimization in features and models, especially the SVM kernel selection and feature selection for different models, this paper concludes that a SVM model with a Radial Basis Function (RBF) kernel can achieve an accuracy rate of 62.51% for the future market trend of the S&P 500 index.展开更多
【目的】研究陕西省的生态安全动态变化,为区域社会经济的可持续发展提供科学依据。【方法】以陕西省为研究区域,在参考国内外已有研究成果的基础上,借助压力-状态-响应模型(P-S-R模型)框架,构建了该区域生态安全评价指标体系,采用理想...【目的】研究陕西省的生态安全动态变化,为区域社会经济的可持续发展提供科学依据。【方法】以陕西省为研究区域,在参考国内外已有研究成果的基础上,借助压力-状态-响应模型(P-S-R模型)框架,构建了该区域生态安全评价指标体系,采用理想解法(Technique for Order Preference by Similarity to Ideal Solution,TOPSIS法),在时间尺度上(1996-2006年)对陕西省的生态安全进行定量评价。【结果】(1)1996-2006年,陕西省生态压力系统安全指数CP值有一定波动,但总体上呈下降趋势,生态负荷加大;状态系统安全指数CS值虽有波动但总体上表现出增长态势,安全状况逐渐好转;响应系统安全指数CR值呈明显的增长趋势,显示陕西省对生态系统的保护能力、保护力度有所增强。(2)陕西省生态安全指数从1996年的0.39增加至2006年的0.60,整体上呈增长趋势,表明生态系统状态由不安全转为较不安全,研究期末(2006年)陕西省的生态安全水平仍处在临界安全边缘。【结论】TOPSIS法简单直观,评价结果客观,且符合区域生态安全变化的实际情况,可用于不同区域生态安全的动态评价。展开更多
基金supported by the Key Project of the Natural Science Foundation of China (Nos.70533050 and 50774084)
文摘In this study we measured the △P(initial speed of gas emission) index with different gas concentrations of carbon dioxide(pure CO2,90% CO2+10% CH4,67% CO2+33% CH4,50% CO2+50% CH4,30% CO2+10% CH4 and pure CH4) of coal samples from the No.2 coal seam in the Yaojie Coal Mine,Gansu province,China.The effect of carbon dioxide concentration,gas composition,coal strength and particle size of coal samples on the △P index was investigated.The experimental results show that with gas of various compositions,the △P value of three samples were clearly different.The △P index of coal samples A,B and C(0.2～0.25 mm) were 4,6 and 7 with pure CH4 and 22,30 and 21 when pure CH4 was used.Carbon dioxide concentration affects the △P index markedly.The △P index increases with an increase in carbon dioxide concentration,especially for coal B.Hence,the △P index and K(another outburst index) values tested only with pure CH4 for prediction of the danger of outburst is not accurate.It is important to determine the initial speed of gas emission given the gas composition of the coal seam to be tested for exact outburst prediction.
文摘Phosphorus (P) risk indices are commonly used in the USA to estimate the field-scale risk of agricultural P runoff. Because the Ohio P Risk Index is increasingly being used to judge farmer performance, it is important to evaluate weighting/scoring of all P Index parameters to ensure Ohio farmers are credited for practices that reduce P runoff risk and not unduly penalized for things not demonstrably related to runoff risk. A sensitivity analysis provides information as to how sensitive the P Index score is to changes in inputs. The objectives were to determine 1) which inputs are most highly associated with P Index scores and 2) the relative impact of each input variable on resultant P Index scores. The current approach uses simulations across 6134 Ohio point locations and five crop management scenarios (CMSs), representing increasing soil disturbance. The CMSs range from all no-till, which is being promoted in Ohio, rotational tillage, which is a common practice in Ohio to full tillage to represent an extreme practice. Results showed that P Index scores were best explained by soil test P (31.9%) followed by connectivity to water (29.7%), soil erosion (13.4%), fertilizer application amount (11.3%), runoff class (9.5%), fertilizer application method (2.2%), and finally filter strip (2.0%). Ohio P Index simulations across CMSs one through five showed that >40% scored <15 points (low) while <1.5% scored >45 points (very high). Given Ohio water quality problems, the Ohio P Index needs to be stricter. The current approach is useful for Ohio P Index evaluations and revision decisions by spatially illustrating the impact of potential changes regionally and state-wide.
文摘Forecasting the movement of stock market is a long-time attractive topic. This paper implements different statistical learning models to predict the movement of S&P 500 index. The S&P 500 index is influenced by other important financial indexes across the world such as commodity price and financial technical indicators. This paper systematically investigated four supervised learning models, including Logistic Regression, Gaussian Discriminant Analysis (GDA), Naive Bayes and Support Vector Machine (SVM) in the forecast of S&P 500 index. After several experiments of optimization in features and models, especially the SVM kernel selection and feature selection for different models, this paper concludes that a SVM model with a Radial Basis Function (RBF) kernel can achieve an accuracy rate of 62.51% for the future market trend of the S&P 500 index.
文摘【目的】研究陕西省的生态安全动态变化,为区域社会经济的可持续发展提供科学依据。【方法】以陕西省为研究区域,在参考国内外已有研究成果的基础上,借助压力-状态-响应模型(P-S-R模型)框架,构建了该区域生态安全评价指标体系,采用理想解法(Technique for Order Preference by Similarity to Ideal Solution,TOPSIS法),在时间尺度上(1996-2006年)对陕西省的生态安全进行定量评价。【结果】(1)1996-2006年,陕西省生态压力系统安全指数CP值有一定波动,但总体上呈下降趋势,生态负荷加大;状态系统安全指数CS值虽有波动但总体上表现出增长态势,安全状况逐渐好转;响应系统安全指数CR值呈明显的增长趋势,显示陕西省对生态系统的保护能力、保护力度有所增强。(2)陕西省生态安全指数从1996年的0.39增加至2006年的0.60,整体上呈增长趋势,表明生态系统状态由不安全转为较不安全,研究期末(2006年)陕西省的生态安全水平仍处在临界安全边缘。【结论】TOPSIS法简单直观,评价结果客观,且符合区域生态安全变化的实际情况,可用于不同区域生态安全的动态评价。
