Experimental Investigation of the Impact of Linear and Nonlinear Information Presentation on Problem Solving

This study investigates the match of technology (iii this case, mode of information presentation) to problem task and the relative importance of each to problem-solving performance. XI: this research, this was achieved by matching information presentation (linear and nonlinear) to problem tasks (spatial and symbolic). The specific focus is on problem-solving performanceinrelationtolinearandnonlinearinformationpresentationandaccess. Italsoexamineswhichcombinationof problem task type arid information presentation yield the best problem-solving performance

The role of presentation format on decision-makers' behaviour in accounting

The recent increase in researching presentation format area is resulting in an increase in awareness of the importance of presentation format on decision-makers' behaviour. This paper presents a synthesis of prior research on presentation format in the accounting literature which could be used as bases and references for future research. It reviews and evaluates existing accounting literature that examines the linkages of presentation format on decision-makers behaviour. Finally, future research opportunities in this area are made

Geometrically nonlinear analysis of the Apollo aft heat shield Final report, 1 Apr. 1966 - 15 Dec. 1966

Structural analysis of Apollo aft heat shield under water impact loading condition

Kinetic Intersections as a Source of Information on Rate Coefficients

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Physics-informed Neural Networks for Solving Inverse Problems of Nonlinear Biot's Equations: Batch Training

In biomedical engineering, earthquake prediction, and underground energy harvesting, it is crucial to indirectly estimate the physical properties of porous media since the direct measurement of those are usually impractical/prohibitive. Here we apply the physics-informed neural networks to solve the inverse problem with regard to the nonlinear Biot's equations. Specifically, we consider batch training and explore the effect of different batch sizes. The results show that training with small batch sizes, i.e., a few examples per batch, provides better approximations (lower percentage error) of the physical parameters than using large batches or the full batch. The increased accuracy of the physical parameters, comes at the cost of longer training time. Specifically, we find the size should not be too small since a very small batch size requires a very long training time without a corresponding improvement in estimation accuracy. We find that a batch size of 8 or 32 is a good compromise, which is also robust to additive noise in the data. The learning rate also plays an important role and should be used as a hyperparameter.Comment: arXiv admin note: text overlap with arXiv:2002.0823