The size and complexity of supercomputer systems and their power and cooling facilities have continuously increased, thus posing additional challenge for long-term and stable operation. Supercomputers are shared computational resources and usually operate with different computational workloads at different locations (space) and timings (time). Better understanding of the supercomputer system's heat generation and cooling behavior is highly desired from the facility operational side for decision making and optimization planning. In this work, we present a dimensionality reduction-based visual analytics method for time-series log data, from supercomputer system and its facility, to capture characteristic spatio-temporal features and behaviors during the operation.

For effective in situ visualization in high performance computing, a viewpoint should be placed close to a critical spot or volume-of-interest (VOI). In order to track unpredictable motions of VOI in simulations, we propose introducing agent-based modeling to the in-situ visualization. In this method, agents are autonomous cameras, and their environment is the simulation. As a demonstration experiment of the agent-based in situ visualization, we put a camera agent to 3D cellular automata. The camera agent successfully tracks a VOI of cells in highly complex time development.

 Present work is devoted to the problem of emergency response to a large-scale disaster and the recovery management of an urban socio-technical system. An iterative algorithm based on reactive decision-making is proposed as an approach enabling the recovery manager to test a set of potential restoration priorities using numerical simulation and agent-based modeling. The proposed method helps to coordinate reconstruction processes even in the case when the information is fluid and uncertain via providing a set of different recovery plans, the efficiency of which may be assessed using various factors. It also enables to determine how an under- or over-evaluation of the damage degree will affect the integral duration of the recovery process. As a result, the recovery manager will be supplied with a collection of possible recovery plans, which are numerically tested, visualized, and whose relative efficiency is evaluated using multiple estimators. Such an outcome enables to avoid the delay of recovery initiation caused by the ambiguity of the initial state of the system and the impossibility to define the recovery strategy right after a disaster occurrence.

An origami structure has the advantage of being light yet stiff and expandable. Some expandable structures are already widely available on the market, but although many have attempted to develop a polyethylene terephthalate (PET) bottle that is foldable in the axial direction without bending, such bottles are not yet on the market. The present work thus aims to develop a PET bottle that easily folds without bending. The initial model is generated with seven layers, of which five layers (i.e., excluding the lid and bottom) are set as spiral cylinder. This model can be folded rather easily without bending. However, the model springs back to almost its original height after compression. Thus, we develop new types of PET bottle with two or three spiral layers to resolve this spring back issue. Our new design can insert the foldable layer into a non-foldable layer, such as a conical shell or cylindrical shell, to suppress the spring back after compression. Additionally, grooves between foldable and non-foldable layers in our new designs can further contribute to the trapping of foldable parts. Moreover, our new design can achieve partial compression to reduce the height of the bottle when the liquid is partially consumed.

Until now, we have considered the Balancing Domain Decomposition DIAGonal scaling (BDD-DIAG) preconditioner as a preconditioner of non-overlapping domain decomposition analysis of 3-dimensional magnetostatic problems taking the magnetic vector potential as an unknown function. One interesting fact is that the direct solver can be used in many cases for solving the coarse problem in BDD-DIAG. In this paper, we show a sufficient condition for positive-definiteness of the coarse matrix in BDD-DIAG of a perturbed maganetostatic problem. Though our numerical results are got in many cases of the original magnetostatic problem, we mainly consider the well-known perturbed problem in this paper. A conjecture is also given for the original magnetostatic problem.

With the development of 3D scanning technology, complex objects in the real world can be recorded efficiently and accurately in the form of large-scale point cloud data. In the scanned point clouds, sharp edges such as contours and soft edges of undulating surfaces enable us to better understand the structure of the complex point clouds. In our previous work, we proposed a deep learning-based point cloud upsampling network that can generate new points to improve the point density in the edge regions, and then combined with our novel transparent visualization method which can effectively improve the visibility of the edge regions in the 3D-scanned point cloud. However, in our previous work, we mainly focused on sharp edges, and for soft edge regions, although the visibility can be improved to some extent, it can be further improved. In this paper, we optimize the upsampling network for soft-edge regions so that the characteristics of the soft-edge regions, such as the point density gradation, can be maintained while upsampling. Moreover, we apply the optimized upsampling network to real scanned data containing numerous soft edges to verify the performance of the upsampling network in the soft edge regions. Experimental results show that the optimized network can maintain the characteristics of soft edge regions while upsampling in the upsampling task for soft edge regions, thus improving the visibility of soft edge regions in transparent visualization more effectively.