Land use/cover change has been recognized as a key component in global change and has attracted increasing attention in recent decades. Scenario simulation of land use change is an important issue in the study of land...Land use/cover change has been recognized as a key component in global change and has attracted increasing attention in recent decades. Scenario simulation of land use change is an important issue in the study of land use/cover change, and plays a key role in land use prediction and policy decision. Based on the remote sensing data of Landsat TM images in 1989, 2000 and 2010, scenario simulation and landscape pattern analysis of land use change driven by socio-economic development and ecological protection policies were reported in Zhangjiakou city, a representative area of the Poverty Belt around Beijing and Tianjin. Using a CLUE-S model, along with socio-economic and geographic data, the land use simulation of four scenarios-namely, land use planning scenario, natural development sce- nario, ecological-oriented scenario and farmland protection scenario-were explored accord- ing to the actual conditions of Zhangjiakou city, and the landscape pattern characteristics under different land use scenarios were analyzed. The results revealed the following： （1） Farmland, grassland, water body and unused land decreased significantly during 1989-2010, with a decrease of 11.09%, 2.82%, 18.20% and 31.27%, respectively, while garden land, forestland and construction land increased over the same period, with an increase of 5.71%, 20.91% and 38.54%, respectively. The change rate and intensity of land use improved in general from 1989 to 2010. The integrated dynamic degree of land use increased from 2.21% during 1989-2000 to 3.96% during 2000-2010. （2） Land use changed significantly throughout 1989-2010. The total area that underwent land use change was 4759.14 km2, accounting for 12.53% of the study area. Land use transformation was characterized by grassland to for- estland, and by farmland to forestland and grassland. （3） Under the land use planning sce- nario, farmland, grassland, water body and unused land shrank significantly, while garden land, forestland and construction land increased. Under the natural d展开更多
An ontology is a conceptualisation of domain knowledge. It is employed in semantic web services technologies to describe the meanings of services so that they can be dynamically searched for and composed according to ...An ontology is a conceptualisation of domain knowledge. It is employed in semantic web services technologies to describe the meanings of services so that they can be dynamically searched for and composed according to their meanings. It is essential for dynamic service discovery, composition, and invocation. Whether an ontology is well constructed has a tremendous impact on the accuracy of the semantic description of a web service, the complexity of the semantic definitions, the efficiency of processing messages passed between services, and the precision and recall rates of service retrieval from service registrations. However, measuring the quality of an ontology remains an open problem. Works on the evaluation of ontologies do exist, but they are not in the context of semantic web services. This paper addresses this problem by proposing a quality model of ontology and defining a set of metrics that enables the quality of an ontology to be measured objectively and quantitatively in the context of semantic descriptions of web services. These metrics cover the contents, presentation, and usage aspects of ontologies. The paper also presents a tool that implements these metrics and reports a case study on five real-life examples of web services.展开更多
A comparative analysis of deep learning models and traditional statistical methods for stock price prediction uses data from the Nigerian stock exchange. Historical data, including daily prices and trading volumes, ar...A comparative analysis of deep learning models and traditional statistical methods for stock price prediction uses data from the Nigerian stock exchange. Historical data, including daily prices and trading volumes, are employed to implement models such as Long Short Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Autoregressive Integrated Moving Average (ARIMA), and Autoregressive Moving Average (ARMA). These models are assessed over three-time horizons: short-term (1 year), medium-term (2.5 years), and long-term (5 years), with performance measured by Mean Squared Error (MSE) and Mean Absolute Error (MAE). The stability of the time series is tested using the Augmented Dickey-Fuller (ADF) test. Results reveal that deep learning models, particularly LSTM, outperform traditional methods by capturing complex, nonlinear patterns in the data, resulting in more accurate predictions. However, these models require greater computational resources and offer less interpretability than traditional approaches. The findings highlight the potential of deep learning for improving financial forecasting and investment strategies. Future research could incorporate external factors such as social media sentiment and economic indicators, refine model architectures, and explore real-time applications to enhance prediction accuracy and scalability.展开更多
功能依赖是大型复杂系统性能或体系能力生成的重要影响因素之一,各类要素实体间功能依赖关系的表示、识别和度量是复杂体系研究的重要问题。通过对网络信息体系(networked information system of systems,NISoS)中要素系统间交互行为的...功能依赖是大型复杂系统性能或体系能力生成的重要影响因素之一,各类要素实体间功能依赖关系的表示、识别和度量是复杂体系研究的重要问题。通过对网络信息体系(networked information system of systems,NISoS)中要素系统间交互行为的依赖性分析,基于功能依赖网络(function dependency network,FDN)构建了NISoS依赖网络模型(dependency network model of NISoS,DNMN)。综合考虑NISoS作战属性与任务完成效果,提出了体系节点重要性的度量指标,在此基础上给出了一种体系重心度量方法。最后,通过某海上方向联合作战NISoS实例计算,演示验证了提出的体系模型和重心度量方法的有效性。展开更多
基金National Natural Science Foundation of China,No.41171088,No.41571087
文摘Land use/cover change has been recognized as a key component in global change and has attracted increasing attention in recent decades. Scenario simulation of land use change is an important issue in the study of land use/cover change, and plays a key role in land use prediction and policy decision. Based on the remote sensing data of Landsat TM images in 1989, 2000 and 2010, scenario simulation and landscape pattern analysis of land use change driven by socio-economic development and ecological protection policies were reported in Zhangjiakou city, a representative area of the Poverty Belt around Beijing and Tianjin. Using a CLUE-S model, along with socio-economic and geographic data, the land use simulation of four scenarios-namely, land use planning scenario, natural development sce- nario, ecological-oriented scenario and farmland protection scenario-were explored accord- ing to the actual conditions of Zhangjiakou city, and the landscape pattern characteristics under different land use scenarios were analyzed. The results revealed the following： （1） Farmland, grassland, water body and unused land decreased significantly during 1989-2010, with a decrease of 11.09%, 2.82%, 18.20% and 31.27%, respectively, while garden land, forestland and construction land increased over the same period, with an increase of 5.71%, 20.91% and 38.54%, respectively. The change rate and intensity of land use improved in general from 1989 to 2010. The integrated dynamic degree of land use increased from 2.21% during 1989-2000 to 3.96% during 2000-2010. （2） Land use changed significantly throughout 1989-2010. The total area that underwent land use change was 4759.14 km2, accounting for 12.53% of the study area. Land use transformation was characterized by grassland to for- estland, and by farmland to forestland and grassland. （3） Under the land use planning sce- nario, farmland, grassland, water body and unused land shrank significantly, while garden land, forestland and construction land increased. Under the natural d
基金supported by the National Natural Science Foundation of China (No. 61502233)Jiangsu Qinglan Projectsupported by EU FP7 project MONICA on Mobile Cloud Computing (No. PIRSES-GA-2011-295222)
文摘An ontology is a conceptualisation of domain knowledge. It is employed in semantic web services technologies to describe the meanings of services so that they can be dynamically searched for and composed according to their meanings. It is essential for dynamic service discovery, composition, and invocation. Whether an ontology is well constructed has a tremendous impact on the accuracy of the semantic description of a web service, the complexity of the semantic definitions, the efficiency of processing messages passed between services, and the precision and recall rates of service retrieval from service registrations. However, measuring the quality of an ontology remains an open problem. Works on the evaluation of ontologies do exist, but they are not in the context of semantic web services. This paper addresses this problem by proposing a quality model of ontology and defining a set of metrics that enables the quality of an ontology to be measured objectively and quantitatively in the context of semantic descriptions of web services. These metrics cover the contents, presentation, and usage aspects of ontologies. The paper also presents a tool that implements these metrics and reports a case study on five real-life examples of web services.
文摘A comparative analysis of deep learning models and traditional statistical methods for stock price prediction uses data from the Nigerian stock exchange. Historical data, including daily prices and trading volumes, are employed to implement models such as Long Short Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Autoregressive Integrated Moving Average (ARIMA), and Autoregressive Moving Average (ARMA). These models are assessed over three-time horizons: short-term (1 year), medium-term (2.5 years), and long-term (5 years), with performance measured by Mean Squared Error (MSE) and Mean Absolute Error (MAE). The stability of the time series is tested using the Augmented Dickey-Fuller (ADF) test. Results reveal that deep learning models, particularly LSTM, outperform traditional methods by capturing complex, nonlinear patterns in the data, resulting in more accurate predictions. However, these models require greater computational resources and offer less interpretability than traditional approaches. The findings highlight the potential of deep learning for improving financial forecasting and investment strategies. Future research could incorporate external factors such as social media sentiment and economic indicators, refine model architectures, and explore real-time applications to enhance prediction accuracy and scalability.
文摘功能依赖是大型复杂系统性能或体系能力生成的重要影响因素之一,各类要素实体间功能依赖关系的表示、识别和度量是复杂体系研究的重要问题。通过对网络信息体系(networked information system of systems,NISoS)中要素系统间交互行为的依赖性分析,基于功能依赖网络(function dependency network,FDN)构建了NISoS依赖网络模型(dependency network model of NISoS,DNMN)。综合考虑NISoS作战属性与任务完成效果,提出了体系节点重要性的度量指标,在此基础上给出了一种体系重心度量方法。最后,通过某海上方向联合作战NISoS实例计算,演示验证了提出的体系模型和重心度量方法的有效性。
