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String hoppers, a largely consumed Indian traditional food, was enriched with extremely nutritious quinoa and processed into its ready-to-cook form. The effects of quinoa flour (25–75 g), rice flour (30–70 g) and guar gum (0–2%) on quality parameters of string hoppers were studied and optimized using Box–Behnken design. Water absorption capacity, color difference and rehydration ratio increased, whereas solid gruel loss decreased with an increase in the amount of quinoa flour. Overall acceptability reflecting sensory attributes of string hoppers reduced at higher levels of quinoa flour. Addition of guar gum improved water absorption capacity and suppressed solid gruel loss. The optimized levels for quinoa flour, rice flour and guar gum were 48.638 g, 30 g and 1.149 g, respectively. The corresponding actual responses of water absorption capacity (62.68%), color difference (23.83), rehydration ratio (1.83), solid gruel loss (1.23%) and overall acceptability (7.27) agree to the prediction of significantly (p <.05) fitted quadratic models. [ABSTRACT FROM AUTHOR]

The popping and puffing are adiabatic expansion process in which the grains are exposed to high temperature for short time. The heat increases vapour pressure inside the grain leading to rupture of outer shell with expansion of starchy endosperm to several folds creating a crispy and aerated puffed snack. Conventional puffing methods use hot air, oil and sand as medium of puffing, whereas nonconventional puffing methods make use of microwave, fluidized air and pressure differential in system. Although it is commonly believed that processing conditions solely influence the quality of expanded grains, many other variables such as physical, chemical and genetic characteristics of grain, post-harvest handling practices, storage conditions and pre-treatments can also largely affect quality indices of expanded grains. As exposed to heat for only short time, expanded grains retain majority of nutrients and even improved digestibility and nutrient bioavailability traits. Flour obtained by milling of expanded grains exhibit desired functional properties expected in bakery products. This paper briefly discussed the factors influencing puffing quality and effects of puffing on physicochemical and morphological characteristics of grain along with its applications. [ABSTRACT FROM AUTHOR]

Autonomous marine vehicles play an essential role in many ocean science and engineering applications. Planning time and energy optimal paths for these vehicles to navigate in stochastic dynamic ocean environments are essential to reduce operational costs. In some missions, they must also harvest solar, wind or wave energy (modeled as a stochastic scalar field) and move in optimal paths that minimize net energy consumption. Markov decision processes (MDPs) provide a natural framework for sequential decision making for robotic agents in such environments. However, building a realistic model and solving the modeled MDP becomes computationally expensive in large-scale real-time applications, warranting the need of parallel algorithms and efficient implementation. In this article, we introduce an efficient end-to-end graphical processing unit (GPU)-accelerated algorithm that 1) builds the MDP model (computing transition probabilities and expected one-step rewards) and 2) solves the MDP to compute an optimal policy. We develop methodical and algorithmic solutions to overcome the limited global memory of GPUs by 1) using a dynamic reduced-order representation of the ocean flows; 2) leveraging the sparse nature of the state transition probability matrix; 3) introducing a neighboring subgrid concept; and 4) proving that it is sufficient to use only the stochastic scalar field’s mean to compute the expected one-step rewards for missions involving energy harvesting from the environment, thereby saving memory and reducing the computational effort. We demonstrate the algorithm on a simulated stochastic dynamic environment and highlight that it builds the MDP model and computes the optimal policy 600–1000 times faster than conventional CPU implementations, making it suitable for real-time use. [ABSTRACT FROM AUTHOR]

The importance of autonomous marine vehicles is increasing in a wide range of ocean science and engineering applications. Multi-objective optimization, where trade-offs between multiple conflicting objectives are achieved (such as minimizing expected mission time, energy consumption, and environmental energy harvesting), is crucial for planning optimal routes in stochastic dynamic ocean environments. We develop a multi-objective path planner in stochastic dynamic flows by further developing and improving our recently developed end-to-end GPU-accelerated single-objective Markov Decision Process path planner. MDPs with scalarized rewards for multiple objectives are formulated and solved in idealized stochastic dynamic ocean environments with dynamic obstacles. Three simulated mission scenarios are completed to elucidate our approach and capabilities: (i) an agent moving from a start to target by minimizing travel time and net-energy consumption when harvesting solar energy in an uncertain flow; (ii) an agent moving from a start to target by minimizing travel time and-energy consumption with uncertainties in obstacle initial positions; (iii) an agent attempting to cross a shipping channel while avoiding multiple fast moving ships in an uncertain flow. Optimal operating curves are computed in a fraction of the time that would be required for existing solvers and algorithms. Crucially, our solution can serve as the benchmark for other approximate AI algorithms such as Reinforcement Learning and help improve explainability of those models. [ABSTRACT FROM AUTHOR]

Accurate digitization of synoptic ocean features is crucial for climate studies and the operational forecasting of ocean and coupled ocean–atmosphere systems. Today, for some North Atlantic operational regional models, skilled human experts visualize and extract the gulf stream and rings (warm and cold eddies) through an extensive and knowledge-based manual process. To automate this task, we develop a dynamics-inspired deep learning system that extracts the Gulf Stream and rings from concurrent satellite images of sea surface temperature (SST) and sea surface height (SSH). We pose the above problem as a multilabel semantic image segmentation problem. A novel deep convolutional neural network architecture named W-Net, with two parallel encoder–decoder branches, is developed to perform the segmentation. The W-Net’s one branch is the SST branch (accepts SST image as input) and another is the SSH branch (accepts SSH as input), and the final output is a segmentation of gulf stream, warm rings, and cold rings. A dataset consisting of SST, SSH, and manual feature annotation (ground truth) from 2014 to 2018 is used for training. For gulf stream, we obtain 82.7% raw test accuracy and a low error of 4.39% in the detected path length. For the Rings, we obtain more than 71% raw eddy detection accuracy. A detailed ablation study and an examination of both SST and SSH parts of the network are presented to understand how the deep neural network learns to segment the gulf stream’s meandering path and Rings accurately. [ABSTRACT FROM AUTHOR]

Non-Gaussian data assimilation is vital for several applications with nonlinear dynamical systems, including geosciences, socio-economics, infectious disease modeling, and autonomous navigation. Widespread adoption of non-Gaussian data assimilation requires easy-to-implement schemes. We develop, implement, and apply an efficient nonlinear non-Gaussian data assimilation scheme using non-intrusive stochastic collocation-based polynomial chaos expansion (PCE) and Gaussian mixture model (GMM) priors fit to the state's uncertainty. First, we represent the uncertainty in a dynamical system using PCE and propagate it using the stochastic collocation method until an assimilation time. Then, we convert the polynomial basis prior to its equivalent Karhunen-Loeve (KL) form, fit a GMM in the subspace and perform a Bayesian filtering step. Thereafter, the posterior polynomial basis is recovered from the posterior GMM in the KL form, and uncertainty propagation is continued using the stochastic collocation method. The derivation and new equations required for the above conversions are presented. We apply the new scheme to an illustrative population growth dynamics application and a complex fluid flow problem for demonstrating its capabilities. In both cases, our filter accurately captures the non-Gaussian statistics compared to the polynomial chaos-ensemble Kalman filter and the polynomial chaos-error subspace statistical estimation filter. [ABSTRACT FROM AUTHOR]

The superfood quinoa is an extremely nutritious and ancient pseudocereal grain particularly known for its protein quality and bioactive compounds, compared to other cereals. Quinoa seeds can be processed into a crispy, cellular-structured and expanded whole-grain snack product by low capital cost involving traditional sand puffing method. The study aims to statistically optimize the processing conditions for sand puffing of quinoa. The moisturetreated and salt-treated quinoa grains were mixed with a high-temperature sand bed for a predetermined time to induce grain expansion. Puffing conditions were optimized using Box-Behnken design by varying the factors such as moisture added (0.2-1.8 ml/10 g grains), salt concentration (0-1%), puffing temperature (200-240 0C) and puffing time (20-60 s). It was found that measured response parameters like puffing yield, expansion ratio, flake size and overall acceptability of puffed quinoa significantly (p<0.05) increased with a decrease in moisture added and salt concentration, and increase in puffing temperature and puffing time. Bulk density of puffed quinoa had a negative correlation with puffing temperature and puffing time. The optimum condition of 0.2 ml moisture added/10 g grains, 0.2% salt concentration, 229 0C puffing temperature and 55 s puffing time was predicted to generate puffing yield of 86.23%, expansion ratio of 3.08, flake size of 8.21 mm3, bulk density of 0.36 g/ml and overall acceptability score of 8.65. The higher yield, three-fold expansion and superior sensory attributes of puffed quinoa achieved from optimized sand puffing condition would benefit the manufactures and be nutritious snack food for consumers to combat malnutrition. [ABSTRACT FROM AUTHOR]

Time-optimal paths are evaluated by VISIR (“discoVerIng Safe and effIcient Routes”), a graph-search ship routing model, with respect to the solution of the fundamental differential equations governing optimal paths in a dynamic wind-wave environment. The evaluation exercise makes use of identical setups: topological constraints, dynamic wave environmental conditions, and vessel-ocean parametrizations, while advection by external currents is not considered. The emphasis is on predicting the time-optimal ship headings and Speeds Through Water constrained by dynamic ocean wave fields. VISIR upgrades regarding angular resolution, time-interpolation, and static navigational safety constraints are introduced. The deviations of the graph-search results relative to the solution of the exact differential equations in both the path duration and length are assessed. They are found to be of the order of the discretization errors, with VISIR’s solution converging to that of the differential equation for sufficient resolution. [ABSTRACT FROM AUTHOR]

In this paper, a reachability-based approach is adopted to deal with the pursuit–evasion differential game between one evader and multiple pursuers in the presence of dynamic environmental disturbances (for example, winds or sea currents). Conditions for the game to be terminated are given in terms of reachable set inclusions. Level set equations are defined and solved to generate the forward reachable sets of the pursuers and the evader. The time-optimal trajectories and the corresponding optimal strategies are subsequently retrieved from these level sets. The pursuers are divided into active pursuers, guards, and redundant pursuers according to their respective roles in the pursuit–evasion game. The proposed scheme is implemented on problems with both simple and realistic time-dependent flowfields, with and without obstacles. [ABSTRACT FROM AUTHOR]

The present study aims to optimize the formulation and frying conditions of Asian-popular prawn chips to reduce the oil uptake and improve the quality of chips. The pregelatinized rice starch content (2–8%), prawn content (3–5%) and frying time (30–60 s) are the independent variables of the experiment based on Box–Behnken design. It was found that pregelatinized rice starch significantly (p < 0.05) reduced the oil uptake (%) of prawn chips. While hardness (g) and chewiness (s) of prawn chips were positively correlated with all independent variables, fracturability (g) was negatively correlated with pregelatinized rice starch content and frying time. An increase in prawn content significantly increased the overall acceptability of chips on a sensory basis. An optimum condition of 3.2% prawn, 8% pregelatinized rice starch and 42 s frying time gave prawn chips 5221.7 g hardness, 3999.2 g fracturability, 112.9 s chewiness, 8.8% oil uptake and 7.5 rating for overall acceptability. [ABSTRACT FROM AUTHOR]