Do Marine Protected Areas Affect Human Nutrition and Health? A Comparison between Villages in Roviana,Solomon Islands

The implementation of marine protected areas (MPAs) for fisheries management has increased recently due to the perceived role of MPAs in conserving biodiversity, increasing fish stocks, and enhancing the food security of coastal communities. However, it is unclear whether MPAs may restrict the availability of marine resources and decrease overall food security and the health of the people. In the Roviana Lagoon of the Solomon Islands, we conducted cross-comparisons of villages with MPAs and a village without an MPA to assess whether MPAs influenced local perceptions of governance, environmental change, livelihood strategies, and actual human nutrition and health. Results showed that residents of villages with effective MPAs had higher energy and protein intake than those who had no MPA or an ineffective MPA. We conclude that “no-take” marine reserves do not have adverse effects and that when MPAs are effectively sustained they may enhance local nutrition and health.     

Addressing the joint occurrence of self-selection and simultaneity biases in the estimation of program effects based on cross-sectional observational surveys: case study of travel behavior effects in carsharing

Transportation - We estimate the effect of carsharing on travel behavior (specifically, household vehicle holdings, frequency of transit usage, and frequency of biking and walking) using data from...     

A hybrid model based method for bus travel time estimation

Providing accurate information about bus arrival time to passengers can make the public transport system more attractive. Such information helps the passengers by reducing the uncertainty on waiting time and the associated frustrations. However, accurate estimation of bus travel time is still a challenging problem, especially under heterogeneous and lane-less traffic conditions. The accuracy of such information provided to passengers depends mainly on the estimation method used, which in turns depends on the input data used. Hence, developing suitable estimation methods and identifying the most significant/appropriate input data are important. The present study focused on these aspects of development of estimation methods that can accurately estimate travel time by using significant inputs. In order to identify significant inputs, a data mining technique, namely the k-NN classifying algorithm, was used. It is based on the similarity in pattern between the input and historic data. These identified inputs were then used in a hybrid model that combined exponential smoothing technique with recursive estimation scheme based on the Kalman Filtering (KF) technique. The optimal values of the smoothing parameter were dynamically estimated and were updated using the latest measurements available from the field. The performance of the proposed algorithm showed a clear improvement in estimation accuracy when compared with existing methods.