Little Ruaha River catchment (6370 Km<sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;">) in the Southern Agricultural</span&g...Little Ruaha River catchment (6370 Km<sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;">) in the Southern Agricultural</span><span style="font-family:;" "=""><span style="font-family:Verdana;"> Growth Corridor of Tanzania (SAGCOT), is one of the country’s most significant waterways due to its ecological composition and economic value. Regardless of its ecological and economical value, the regional hydrologic condition has been tremendously affected due to land uses alteration, influenced by different socio-economic factors. This study aimed to understand the associated impacts of the present Land Use Land Cover (LULC) change on the surface runoff and sediment yield in the Little Ruaha River Catchment. Hydrological modelling using Soil and Water Assessment Tool (SWAT Model) was done to quantify the impact of land use and land cover dynamics on catchment water </span><span style="font-family:Verdana;">balance and sediment loads. The calibration and validation of the SWAT</span><span style="font-family:Verdana;"> model were performed using sequential uncertainty fitting (SUFI-2). The results showed that, for the given LULC change, the average annual surface runoff increased by 2.78 mm while average annual total sediment loading increased by 3.56 t/ha, the average annual base flow decreased by 2.68 mm, ground water shallow aquifer recharge decreased from 2.97 mm and a slight decrease in average annual ground water deep aquifer recharge by 0.14 mm. The model predicts that in the future, there will be a further increase in both surface runoff and sediment load. Such changes, increased runoff generation and sediment yield with decreased base flow have implications on the sustenance flow regimes particularly the observed reduced dry season river flow of the Little Ruaha River, which in turn cause adverse impacts to the biotic component of the ecosystem, reduced water storage and energy production at Mtera Hydroelectrical dam also increasing the chances of flooding at some times of the year. The study recommends land use planning at the village level, and conservation agricultural practices to ameliorate the current situation. Developing multidisciplinary approaches for integrated catchment management is the key to the sustainability of Little Ruaha River catchment.</span></span>展开更多
Common prosperity,an important goal of human development,increasingly has to be achieved through the common prosperity of cities.The research into and discovery of the determinants which create differences in urban ec...Common prosperity,an important goal of human development,increasingly has to be achieved through the common prosperity of cities.The research into and discovery of the determinants which create differences in urban economic competitiveness is of great signi ficance as,thereby,the research and expected discovery can help with the formulation of relevant policies for development,competition,and cooperation to promote win-win conditions among cities.However,such research is still rare.Based on the economic competitiveness data of 1,007 cities,this paper uses OLS regression and Shapley-value based decompositions regression to analyze factors affecting the economic competitiveness of global cities and differences that the cities made.Combined with quantile regression,studying the law of changing of each factor's effect on cities with different levels of economic competitiveness is of theoretical and practical signi ficance.The study findings are as follows.(1)The variability of global urban economic competitiveness is quite large.Cities in North America and Europe are still the benchmarks of global urban economic prosperity.(2)Financial services,technological innovation,industrial system,business environment,institutional environment,infrastructure,among other factors,have signi ficant impacts on the economic competitiveness of cities.(3)The primary factor that in fluenced the variability in economic competitiveness is technological innovation.(4)The ranking of the main in fluencing factors varied slightly between cities at different levels of economic competitiveness.These indicate that the international community should promote innovation in and diffusion of science and technology to achieve common prosperity by narrowing the gap between cities.The relevant decision-making departments,e.g.urban planning departments with strong economic and finance expertise of the cities in different development zones should adopt different measures in accordance with their actual situations.展开更多
The present work deals with the research of chemical constituents and evaluation of antioxidant properties of Bebotho propolis. From the ethyl acetate extract, we isolated, using various chromatographic techniques, a ...The present work deals with the research of chemical constituents and evaluation of antioxidant properties of Bebotho propolis. From the ethyl acetate extract, we isolated, using various chromatographic techniques, a mixture of two identical compounds (isomers) indexed PBy4a and PByb. The structures of these compounds were elucidated by means of spectroscopic analysis techniques (MS, IR, <sup>1</sup>H-NMR, <sup>13</sup>C-NMR, HMBC and HSQC) and by comparison of the spectral data with those described in the literature. Thus, these compounds were identified to a mixture of two chromones namely 5,7-dihydroxy-2-methylchromone-6-C-α-D-glucopyranoside and 5,7-dihydroxy-2-methylchromone-8-C-β-D-glucopyranoside, first reported in propolis. The study of the antiradical power, chelating power and the quantification of phenolic compound of these same extracts, showed interesting properties that propolis extracts have to scavenge free radicals.展开更多
The purpose of this study is to investigate mass valuation of unimproved land value using machine learning techniques. The study was conducted in Nairobi County. It is one of the 47 Kenyan Counties under the 2010 cons...The purpose of this study is to investigate mass valuation of unimproved land value using machine learning techniques. The study was conducted in Nairobi County. It is one of the 47 Kenyan Counties under the 2010 constitution. A total of 1440 geocoded data points containing the market selling price of vacant land in Nairobi were web scraped from major property listing websites. These data points were adopted as dependent variables given as unit price of vacant land per square meter. The Covariates used in this study were categorized into Accessibility, Environmental, Physical and Socio-Economic Factors. Due to multi-collinearity problem present in the covariates, PLS and PCA methods were adopted to transform the observed features using a set of vectors. These methods resulted in an uncorrelated set of components that were used in training machine learning algorithms. The dependent variable and uncorrelated components derived feature reduction methods were used as training data for training different machine learning regression models namely;Random forest, support vector regression and extreme gradient boosting regression (XGboost regression). PLS performed better than PCA because the former maximizes the covariance between dependent and independent variables while the latter maximizes variance between the independent variables only and ignores the relationship between predictors and response. The first 9 components were identified as significant both by PLS and PCA methods. The spatial distribution of vacant land value within Nairobi County was consistent for all the three machine learning models. It was also noted that the land value pattern was higher in the central business district and the pattern spread northwards and westwards relative to the CBD. A relative low vacant land value pattern was observed on the eastern side of the county and also at the extreme periphery of Nairobi County boundary. From the accuracy metrics of R-squared and MAPE, Random Forest Regression model performed better than XGBoost and SVR models. This confirms the capability of random forest model to predict valid estimates of vacant land value for purposes of property taxation in Nairobi County.展开更多
文摘Little Ruaha River catchment (6370 Km<sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;">) in the Southern Agricultural</span><span style="font-family:;" "=""><span style="font-family:Verdana;"> Growth Corridor of Tanzania (SAGCOT), is one of the country’s most significant waterways due to its ecological composition and economic value. Regardless of its ecological and economical value, the regional hydrologic condition has been tremendously affected due to land uses alteration, influenced by different socio-economic factors. This study aimed to understand the associated impacts of the present Land Use Land Cover (LULC) change on the surface runoff and sediment yield in the Little Ruaha River Catchment. Hydrological modelling using Soil and Water Assessment Tool (SWAT Model) was done to quantify the impact of land use and land cover dynamics on catchment water </span><span style="font-family:Verdana;">balance and sediment loads. The calibration and validation of the SWAT</span><span style="font-family:Verdana;"> model were performed using sequential uncertainty fitting (SUFI-2). The results showed that, for the given LULC change, the average annual surface runoff increased by 2.78 mm while average annual total sediment loading increased by 3.56 t/ha, the average annual base flow decreased by 2.68 mm, ground water shallow aquifer recharge decreased from 2.97 mm and a slight decrease in average annual ground water deep aquifer recharge by 0.14 mm. The model predicts that in the future, there will be a further increase in both surface runoff and sediment load. Such changes, increased runoff generation and sediment yield with decreased base flow have implications on the sustenance flow regimes particularly the observed reduced dry season river flow of the Little Ruaha River, which in turn cause adverse impacts to the biotic component of the ecosystem, reduced water storage and energy production at Mtera Hydroelectrical dam also increasing the chances of flooding at some times of the year. The study recommends land use planning at the village level, and conservation agricultural practices to ameliorate the current situation. Developing multidisciplinary approaches for integrated catchment management is the key to the sustainability of Little Ruaha River catchment.</span></span>
文摘Common prosperity,an important goal of human development,increasingly has to be achieved through the common prosperity of cities.The research into and discovery of the determinants which create differences in urban economic competitiveness is of great signi ficance as,thereby,the research and expected discovery can help with the formulation of relevant policies for development,competition,and cooperation to promote win-win conditions among cities.However,such research is still rare.Based on the economic competitiveness data of 1,007 cities,this paper uses OLS regression and Shapley-value based decompositions regression to analyze factors affecting the economic competitiveness of global cities and differences that the cities made.Combined with quantile regression,studying the law of changing of each factor's effect on cities with different levels of economic competitiveness is of theoretical and practical signi ficance.The study findings are as follows.(1)The variability of global urban economic competitiveness is quite large.Cities in North America and Europe are still the benchmarks of global urban economic prosperity.(2)Financial services,technological innovation,industrial system,business environment,institutional environment,infrastructure,among other factors,have signi ficant impacts on the economic competitiveness of cities.(3)The primary factor that in fluenced the variability in economic competitiveness is technological innovation.(4)The ranking of the main in fluencing factors varied slightly between cities at different levels of economic competitiveness.These indicate that the international community should promote innovation in and diffusion of science and technology to achieve common prosperity by narrowing the gap between cities.The relevant decision-making departments,e.g.urban planning departments with strong economic and finance expertise of the cities in different development zones should adopt different measures in accordance with their actual situations.
文摘The present work deals with the research of chemical constituents and evaluation of antioxidant properties of Bebotho propolis. From the ethyl acetate extract, we isolated, using various chromatographic techniques, a mixture of two identical compounds (isomers) indexed PBy4a and PByb. The structures of these compounds were elucidated by means of spectroscopic analysis techniques (MS, IR, <sup>1</sup>H-NMR, <sup>13</sup>C-NMR, HMBC and HSQC) and by comparison of the spectral data with those described in the literature. Thus, these compounds were identified to a mixture of two chromones namely 5,7-dihydroxy-2-methylchromone-6-C-α-D-glucopyranoside and 5,7-dihydroxy-2-methylchromone-8-C-β-D-glucopyranoside, first reported in propolis. The study of the antiradical power, chelating power and the quantification of phenolic compound of these same extracts, showed interesting properties that propolis extracts have to scavenge free radicals.
文摘The purpose of this study is to investigate mass valuation of unimproved land value using machine learning techniques. The study was conducted in Nairobi County. It is one of the 47 Kenyan Counties under the 2010 constitution. A total of 1440 geocoded data points containing the market selling price of vacant land in Nairobi were web scraped from major property listing websites. These data points were adopted as dependent variables given as unit price of vacant land per square meter. The Covariates used in this study were categorized into Accessibility, Environmental, Physical and Socio-Economic Factors. Due to multi-collinearity problem present in the covariates, PLS and PCA methods were adopted to transform the observed features using a set of vectors. These methods resulted in an uncorrelated set of components that were used in training machine learning algorithms. The dependent variable and uncorrelated components derived feature reduction methods were used as training data for training different machine learning regression models namely;Random forest, support vector regression and extreme gradient boosting regression (XGboost regression). PLS performed better than PCA because the former maximizes the covariance between dependent and independent variables while the latter maximizes variance between the independent variables only and ignores the relationship between predictors and response. The first 9 components were identified as significant both by PLS and PCA methods. The spatial distribution of vacant land value within Nairobi County was consistent for all the three machine learning models. It was also noted that the land value pattern was higher in the central business district and the pattern spread northwards and westwards relative to the CBD. A relative low vacant land value pattern was observed on the eastern side of the county and also at the extreme periphery of Nairobi County boundary. From the accuracy metrics of R-squared and MAPE, Random Forest Regression model performed better than XGBoost and SVR models. This confirms the capability of random forest model to predict valid estimates of vacant land value for purposes of property taxation in Nairobi County.
