Bangladesh remains one of the most vulnerable countries in the world to the effects of climate change. Given the reliance of a large segment of the population on the agricultural sector for both their livelihoods as well as national food security, climate change adaptation in the agricultural sector is crucial for continued national food security and economic growth. Using household data from lowland rice farmers of selected haor areas in Sylhet, the current work presents an analysis of the determinants behind the implementation of different climate change adaptation strategies by lowland rice farmers. The first objective of this study was to explore the extent of awareness of climate change within this population as well as the type of opinions held by lowland rice farmers with respect to climate change. To serve this purpose, a severity index (SI) was developed and subsequently employed to evaluate the perceptions and attitudes of 378 farmers with respect to climate change vulnerability. Respondents were interviewed with respect to climate change related circumstances they faced in their daily lives. Attained SI index values ranged from 69.18% to 93.52%. The SI for the perception “Climate change affects rice production” was measured as 93.52%. Using data collected from the same 378 farmers, a logistic regression was carried out to investigate the impact of socio-economic and institutional factors on adaptation. The results show that credit from non-government organizations is highly statistically significant for adaptation, and that rural market structure also has a positive effect on adaptation. Among the studied factors, credit from non-governmental organizations (NGOs) was found to be the most important factor for adaptation. The results of this work further indicate that marginal farmers would benefit from government (GoB) funded seasonal training activities that cover pertinent information regarding adaptation after flash floods. Additionally, the authors of this piece recommend timely issuance of government-assisted credit during early flash floods to afflicted farmers, as such an initiative can aid farmers in adapting different strategies to mitigate losses and enhance their productivity as well as livelihood.

The objective of this paper is to apply the Translog Stochastic Frontier production model (SFA) and Data Envelopment Analysis (DEA) to estimate efficiencies over time and the Total Factor Productivity (TFP) growth rate for Bangladeshi rice crops (Aus, Aman and Boro) throughout the most recent data available comprising the period 1989–2008. Results indicate that technical efficiency was observed as higher for Boro among the three types of rice, but the overall technical efficiency of rice production was found around 50%. Although positive changes exist in TFP for the sample analyzed, the average growth rate of TFP for rice production was estimated at almost the same levels for both Translog SFA with half normal distribution and DEA. Estimated TFP from SFA is forecasted with ARIMA (2, 0, 0) model. ARIMA (1, 0, 0) model is used to forecast TFP of Aman from DEA estimation.

Pedestrian movements are the consequence of several complex and stochastic facts. The modelling of pedestrian movements and the ability to predict the travel time are useful for evaluating the performance of a pedestrian facility. However, only a few studies can be found that incorporate the design of the facility, local pedestrian body dimensions, the delay experienced by the pedestrians, and level of service to the pedestrian movements. In this paper, a queuing based analytical model is developed as a function of relevant determinants and functional factors to predict the travel time on pedestrian facilities. The model can be used to assess the overall serving rate or performance of a facility layout and correlate it to the level of service that is possible to provide the pedestrians. It has also the ability to provide a clear suggestion on the designing and sizing of pedestrian facilities. The model is empirically validated and is found to be a robust tool to understand how well a particular walking facility makes possible comfort and convenient pedestrian movements. The sensitivity analysis is also performed to see the impact of some crucial parameters of the developed model on the performance of pedestrian facilities.

M/G/C/C state dependent queuing networks consider service rates as a function of the number of residing entities (e.g., pedestrians, vehicles, and products). However, modeling such dynamic rates is not supported in modern Discrete Simulation System (DES) software. We designed an approach to cater this limitation and used it to construct the M/G/C/C state-dependent queuing model in Arena software. Using the model, we have evaluated and analyzed the impacts of various arrival rates to the throughput, the blocking probability, the expected service time and the expected number of entities in a complex network topology. Results indicated that there is a range of arrival rates for each network where the simulation results fluctuate drastically across replications and this causes the simulation results and analytical results exhibit discrepancies. Detail results that show how tally the simulation results and the analytical results in both abstract and graphical forms and some scientific justifications for these have been documented and discussed.