A B S T R A C TAn approximation is developed that allows mapped 4D seismic amplitudes and timeshifts to be related directly to the weighted linear sum of pore pressure and saturation changes. The weights in this relation are identified as key groups of parameters from a petroelastic model and include the reservoir porosity. This dependence on groups of parameters explains the inherent non-uniqueness of this problem experienced by previous researchers. The proposed relation is of use in 4D seismic data feasibility studies and inversion and interpretation of the 4D seismic response in terms of pore pressure and water saturation changes. A further result is drawn from analysis of data from the North Sea and West Africa, which reveals that the relative interplay between the effects of pore pressure and saturation changes on the seismic data can be simplified to the control of a single, spatially variant parameter C S /C P . Combining these results with those from published literature, we find that C S /C P = 8 appears to be a generality across a range of clastic reservoirs with a similar mean porosity. Using this C S /C P value, an in situ seismic-scale constraint for the rock stress sensitivity component of the petroelastic model is constructed considering this component carries the largest uncertainty.

The present research aims to include the native Peruvian fruits in the design of drinks with low caloric content. For this purpose, four types of mixed nectares are proposed: A (Pineapple, Camu-camu, Apple and extract of purple Maize), B (Manzana, Sanky and Camu-camu), C (Maracuyá, Mango and Aguaymanto) and D Pineapple and Camu-camu), with a sucrose substitution factor by Stevia of 50%, obtained by a relative intensity of sweetness test. For the selection of the nectar types, a preference classification test was used with 120 panels composed of 18-21 year old students and the data were analyzed with theFriedman test and the Kramer test (α = 0.05), where Obtained formulation A, consisting of 27.9% pulp mixture, 10% corn extract, 57.6% water, 4.06% sugar, 0.19% acidulant, 0.05% Antioxidant, 0.12% stabilizer and 0.05% Stevia powder, as the most preferred nectar. To evaluate its quality, the proximal chemical, physicochemical, microbiological, sensorial acceptability tests were tested using a hedonic scale of 9 points, and its useful life through accelerated tests, using the quality parameters as an indicator of deterioration. From the quality tests, a pH of 3.37 ± 0.08, Brix 6.53 ± 0.30, acidity 0.2868 ± 0.0061, density 1.035 ± 0.006, fats 0.4%, carbohydrates 8.8%, proteins 0.4%, fiber 0.4%, vitamin C 57.4 Mg / 100 g and no caloric content of 40.4 Kcal / 100 g. For a greater acceptability of the drinks it is established that the percentages of incorporation of the fruits are equal to or less than 7.40% in camu-camu, 20.14% in aguaymanto, and 16.67% in sanky, the options may vary depending on the fruits that accompany The formulation. Also, the presence of stevia as a substitute for sucrose by 50% did not give a significant difference in sweetness.

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By relabeling past experience with heuristic or curriculum goals, state-of-the-art reinforcement learning (RL) algorithms such as hindsight experience replay (HER), hindsight goal generation (HGG), and graph-based hindsight goal generation (G-HGG) have been able to solve challenging robotic manipulation tasks in multi-goal settings with sparse rewards. HGG outperforms HER in challenging tasks in which goals are difficult to explore by learning from a curriculum, in which intermediate goals are selected based on the Euclidean distance to target goals. G-HGG enhances HGG by selecting intermediate goals from a precomputed graph representation of the environment, which enables its applicability in an environment with stationary obstacles. However, G-HGG is not applicable to manipulation tasks with dynamic obstacles, since its graph representation is only valid in static scenarios and fails to provide any correct information to guide the exploration. In this paper, we propose bounding box-based hindsight goal generation (Bbox-HGG), an extension of G-HGG selecting hindsight goals with the help of image observations of the environment, which make it applicable to tasks with dynamic obstacles. We evaluate Bbox-HGG on four challenging manipulation tasks, where significant enhancements in both sample efficiency and overall success rate are shown over state-of-the-art algorithms. The videos can be viewed at https://videoviewsite.wixsite.com/bbhgg.