Pressure-sensitive dissipation in elastomers and its implications for the detonation of plastic explosives

The role of binder deformation and the associated energy dissipation on the detonation sensitivity of plastically bonded explosives is considered by accounting for dilatation-sensitive viscoelastic shear response. Following the observation that pressurization can prolong the relaxation and retardation times of a viscoelastic elastomer tremendously, the implications of this phenomenon are considered for a thin layer of a model elastomer, sheared between two blocks of octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine under deformation rates typical in detonation scenarios. The consequences of concurrent pressurization on heat generation are examined using small deformation as well as finite deformation analyses. While a dilatation-insensitive viscoelastic behavior generates notable temperature increases, they are insufficient to cause ignition of the explosive. However, taking into account the increased dissipation associated with the pressure-induced changes in the intrinsic time scale and viscosity of the elastomer leads to temperature rises on the order of 1000degreesC, which are consistent with "hot spots" held responsible for the initiation of detonation in the adjacent explosive grains

Does a language model “understand” high school math? A survey of deep learning based word problem solvers

From the latter half of the last decade, there has been a growing interest in developing algorithms for automatically solving mathematical word problems (MWP). It is a challenging and unique task that demands blending surface level text pattern recognition with mathematical reasoning. In spite of extensive research, we still have a lot to explore for building robust representations of elementary math word problems and effective solutions for the general task. In this paper, we critically examine the various models that have been developed for solving word problems, their pros and cons and the challenges ahead. In the last 2 years, a lot of deep learning models have recorded competing results on benchmark datasets, making a critical and conceptual analysis of literature highly useful at this juncture. We take a step back and analyze why, in spite of this abundance in scholarly interest, the predominantly used experiment and dataset designs continue to be a stumbling block. From the vantage point of having analyzed the literature closely, we also endeavor to provide a road‐map for future math word problem research. This article is categorized under: Technologies > Machine Learning Technologies > Artificial Intelligence Fundamental Concepts of Data and Knowledge > Knowledge Representation