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The 150 nm thick, (0001) orientated wurtzite-phase Al1−xInxN epitaxial layers were grown by metal organic chemical vapor deposition on GaN (2.3 µm) template/(0001) sapphire substrate. The indium (x) concentration of the Al1−xInxN epitaxial layers was changed as 0.04, 0.18, 0.20, 0.47, and 0.48. The Indium content (x), lattice parameters, and strain values in the AlInN layers were calculated from the reciprocal lattice mapping around symmetric (0002) and asymmetric (10–15) reflection of the AlInN and GaN layers. The mosaic structure characteristics of the AlInN layers, such as lateral and vertical coherence lengths, tilt and twist angle, heterogeneous strain, and dislocation densities (edge and screw type dislocations) of the AlInN epilayers, were investigated by using high-resolution X-ray diffraction measurements and with a combination of Williamson-Hall plot and the fitting of twist angles.

The complexity of most power grid simulation algorithms scales with the network size, which corresponds to the number of buses and branches in the grid. Parallel and distributed computing is one approach that can be used to achieve improved scalability. However, the efficiency of these algorithms requires an optimal grid partitioning strategy. To obtain the requisite power grid partitionings, the authors first apply several graph theory based partitioning algorithms, such as the Karlsruhe fast flow partitioner (KaFFPa), spectral clustering, and METIS. The goal of this study is an examination and evaluation of the impact of grid partitioning on power system problems. To this end, the computational performance of AC optimal power flow (OPF) and dynamic power grid simulation are tested. The partitioned OPF‐problem is solved using the augmented Lagrangian based alternating direction inexact Newton method, whose solution is the basis for the initialisation step in the partitioned dynamic simulation problem. The computational performance of the partitioned systems in the implemented parallel and distributed algorithms is tested using various IEEE standard benchmark test networks. KaFFPa not only outperforms other partitioning algorithms for the AC OPF problem, but also for dynamic power grid simulation with respect to computational speed and scalability.

In this work, we established the foundations of a framework with the goal to build an end-to-end naturalistic expressive listening agent. The project was split into modules for recognition of the user’s paralinguistic and nonverbal expressions, prediction of the agent’s reactions, synthesis of the agent’s expressions and data recordings of nonverbal conversation expressions. First, a multimodal multitask deep learning-based emotion classification system was built along with a rule-based visual expression detection system. Then several sequence prediction systems for nonverbal expressions were implemented and compared. Also, an audiovisual concatenation-based synthesis system was implemented. Finally, a naturalistic, dyadic emotional conversation database was collected. We report here the work made for each of these modules and our planned future improvements.