Research on learning difficulties in mathematics adopts one of two distinct perspectives. According to the first, learning difficulties are due to the intrinsic characteristics of the student. For supporters of the se...Research on learning difficulties in mathematics adopts one of two distinct perspectives. According to the first, learning difficulties are due to the intrinsic characteristics of the student. For supporters of the second perspective, those difficulties result from the interaction between the student and the school system. The objective of this study is to test the validity of these two perspectives in interpreting learning difficulties in mathematics among at-risk students. To this end, we collaborated with normally achieving （undiagnosed） and at-risk students. Results show that the second perspective is better suited to the interpretation of learning difficulties in mathematics students展开更多
Cities are centers of socioeconomic activities,and transport networks carry cargoes and passengers from one city to another. However, transport networks are influenced by meteorological hazards, such as rainstorms,hur...Cities are centers of socioeconomic activities,and transport networks carry cargoes and passengers from one city to another. However, transport networks are influenced by meteorological hazards, such as rainstorms,hurricanes, and fog. Adverse weather impacts can easily spread over a network. Existing models evaluating such impacts usually neglect the transdisciplinary nature of approaches for dealing with this problem. In this article, a mesoscopic mathematical model is proposed to quantitatively assess the adverse impact of rainstorms on a regional transport network in northern China by measuring the reduction in traffic volume. The model considers four factors: direct and secondary impacts of rainstorms, interdependency between network components, and recovery abilities of cities. We selected the Beijing-Tianjin-Hebei region as the case study area to verify our model.Socioeconomic, precipitation, and traffic volume data in this area were used for model calibration and validation.The case study highlights the potential of the proposed model for rapid disaster loss assessment and risk reduction planning.展开更多
文摘Research on learning difficulties in mathematics adopts one of two distinct perspectives. According to the first, learning difficulties are due to the intrinsic characteristics of the student. For supporters of the second perspective, those difficulties result from the interaction between the student and the school system. The objective of this study is to test the validity of these two perspectives in interpreting learning difficulties in mathematics among at-risk students. To this end, we collaborated with normally achieving （undiagnosed） and at-risk students. Results show that the second perspective is better suited to the interpretation of learning difficulties in mathematics students
基金sponsored by the National Science Foundation of China Youth Project (#41401599)the National Basic Research Program of China (2012CB955402)+2 种基金the Beijing Municipal Science and Technology Commission (Z151100002115040)the International Cooperation Project (2012DFG20710)the International Center of Collaborative Research on Disaster Risk Reduction
文摘Cities are centers of socioeconomic activities,and transport networks carry cargoes and passengers from one city to another. However, transport networks are influenced by meteorological hazards, such as rainstorms,hurricanes, and fog. Adverse weather impacts can easily spread over a network. Existing models evaluating such impacts usually neglect the transdisciplinary nature of approaches for dealing with this problem. In this article, a mesoscopic mathematical model is proposed to quantitatively assess the adverse impact of rainstorms on a regional transport network in northern China by measuring the reduction in traffic volume. The model considers four factors: direct and secondary impacts of rainstorms, interdependency between network components, and recovery abilities of cities. We selected the Beijing-Tianjin-Hebei region as the case study area to verify our model.Socioeconomic, precipitation, and traffic volume data in this area were used for model calibration and validation.The case study highlights the potential of the proposed model for rapid disaster loss assessment and risk reduction planning.
