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A numerical study of three-dimensional heat transfer and fluid flow in a moving gas metal arc welding (GMAW) process is performed by considering various driving forces of fluid flow such as buoyancy, Lorentz force, and surface tension. The computation of the current density distribution and the resulting Lorentz force field is performed by solving the Maxwell equations numerically in the domain of the workpiece. The phase change process during melting and solidification is modeled using the enthalpy-porosity technique. Mass and energy transports by droplet transfer are also considered through a thermal analysis of the electrode. The droplet heat addition to the molten pool is considered to be a volumetric heat source distributed in an imaginary cylindrical cavity within the weld pool ("cavity" model). This nature of the heat source distributed due to the falling droplets takes into account the momentum and thermal energy of the droplets. The numerical model is able to capture the well-known "finger" penetration commonly observed in the GMAW process. Numerical prediction regarding the weld pool shape and size is compared with the corresponding experimental results, showing good qualitative agreement between the two. The weld pool geometry is also found to be dependant on some key parameters of welding, such as the torch speed and power input to the workpiece. [ABSTRACT FROM AUTHOR]

Background: It is widely believed that tertiary nucleotide-nucleotide interactions are essential in determining RNA structure and function. Currently, direct coupling analysis (DCA) infers nucleotide contacts in a sequence from its homologous sequence alignment across different species. DCA and similar approaches that use sequence information alone typically yield a low accuracy, especially when the available homologous sequences are limited. Therefore, new methods for RNA structural contact inference are desirable because even a single correctly predicted tertiary contact can potentially make the difference between a correct and incorrectly predicted structure. Here we present a new method DIRECT (Direct Information REweighted by Contact Templates) that incorporates a Restricted Boltzmann Machine (RBM) to augment the information on sequence co-variations with structural features in contact inference., Results: Benchmark tests demonstrate that DIRECT achieves better overall performance than DCA approaches. Compared to mfDCA and plmDCA, DIRECT produces a substantial increase of 41 and 18%, respectively, in accuracy on average for contact prediction. DIRECT improves predictions for long-range contacts and captures more tertiary structural features., Conclusions: We developed a hybrid approach that incorporates a Restricted Boltzmann Machine (RBM) to augment the information on sequence co-variations with structural templates in contact inference. Our results demonstrate that DIRECT is able to improve the RNA contact prediction.