Testing for sensory threshold in drinking water with added calcium: a first step towards developing a calcium fortified water

Background: Food fortification is an effective strategy that has been recommended for improving population calcium inadequate intakes. Increasing calcium concentration of water has been proposed as a possible strategy to improve calcium intake. The objective of this study was to determine the sensory threshold of different calcium salts added to drinking water using survival analysis. Methods: We performed the triangle test methodology for samples of water with added calcium using three different calcium salts: calcium chloride, calcium gluconate and calcium lactate. For each salt, a panel of 54 consumers tested seven batches of three water samples. Data were adjusted for chance and sensory threshold was estimated using the survival methodology and a discrimination of 50%. Results: The threshold value estimation for calcium gluconate was 587 ± 131 mg/L of water, corresponding to 25% discrimination, for calcium lactate was 676 ± 186 mg/L, corresponding to 50% discrimination, and for calcium chloride was 291 ± 73 mg/L, corresponding to 50% discrimination. Conclusions: These results show that water with calcium added in different salts and up to a concentration of 500 mg of calcium/L of water is feasible. The calcium salt allowing the highest calcium concentration with the lowest perceived changes in taste was calcium gluconate. Future studies need to explore stability and acceptability over longer periods of time.

Value Iteration Networks with Double Estimator for Planetary Rover Path Planning

Path planning technology is significant for planetary rovers that perform exploration missions in unfamiliar environments. In this work, we propose a novel global path planning algorithm, based on the value iteration network (VIN), which is embedded within a differentiable planning module, built on the value iteration (VI) algorithm, and has emerged as an effective method to learn to plan. Despite the capability of learning environment dynamics and performing long-range reasoning, the VIN suffers from several limitations, including sensitivity to initialization and poor performance in large-scale domains. We introduce the double value iteration network (dVIN), which decouples action selection and value estimation in the VI module, using the weighted double estimator method to approximate the maximum expected value, instead of maximizing over the estimated action value. We have devised a simple, yet effective, two-stage training strategy for VI-based models to address the problem of high computational cost and poor performance in large-size domains. We evaluate the dVIN on planning problems in grid-world domains and realistic datasets, generated from terrain images of a moon landscape. We show that our dVIN empirically outperforms the baseline methods and generalize better to large-scale environments.

Threshold value estimation of journey-distance using generalized polynomial function

The present work demonstrates an experience in estimating the threshold value of journey distances travelled by transit passengers using generalized polynomial function. The threshold value of journey distances may be defined as that distance beyond which passengers might no more be interested to travel by their reported mode. A knowledge on this threshold value is realized to be useful to limit the upper-most slab of transit fare, while preparing of a length-based fare matrix table. Theoretically, the threshold value can be obtained at that point on the cumulative frequency distribution (CFD) curve of journey distances at which the maximum rate of change of the slope of curve occurs. In this work, the CFD curve of the journey distance values is empirically modelled using Newton’s Polynomial Interpolation method, which helps to overcome various challenges usually encountered while an assumption of a theoretical probability distribution is considered a priori for the CFD.