Structural analysis is essential in modern industrial design, where engineers iteratively refine geometry models based on stress simulations to achieve optimized designs. However, comparing stress distributions across multiple model variants remains challenging due to the complexity of stress fields, which are high-dimensional, unevenly distributed, and dependent on intricate geometric structures. Existing tools primarily support single-model analysis and lack dedicated functionalities for multi-model comparison. As a result, engineers must rely on manual, cognitively demanding visual inspections, making it difficult to systematically identify and interpret stress variations across design iterations. To address these limitations, we propose StressDiffVis, a visual analytics approach that facilitates stress field comparison across multiple structural models. StressDiffVis employs a volumetric representation to encode stress distributions while minimizing occlusion, enabling voxel-wise difference analysis for model comparison. To support localized analysis, we introduce model segmentation, grouping voxels with similar stress patterns across models. StressDiffVis integrates these techniques into an interactive interface with a tree view, organizing models by the iterative design process, and a comparison view, using a matrix layout for detailed comparisons. We demonstrate the effectiveness of StressDiffVis through two case studies illustrating its utility in comparative stress analysis. In addition, expert interviews confirm its potential to enhance engineering workflows.

Spatial time series visualization offers scientific research pathways and analytical decision-making tools across various spatiotemporal domains. Despite many advanced methodologies, the seamless integration of temporal and spatial information remains a challenge. The space-time cube (STC) stands out as a promising approach for the synergistic presentation of spatial and temporal information, with successful applications across various spatiotemporal datasets. However, the STC is plagued by well-known issues such as visual occlusion and depth ambiguity, which are further exacerbated when dealing with large-scale spatial time series data. In this study, we introduce a novel technical framework termed VolumeSTCube, designed for continuous spatiotemporal phenomena. It first leverages the concept of the STC to transform discretely distributed spatial time series data into continuously volumetric data. Subsequently, volume rendering and surface rendering techniques are employed to visualize the transformed volumetric data. Volume rendering is utilized to mitigate visual occlusion, while surface rendering provides pattern details by enhanced lighting information. Lastly, we design interactions to facilitate the exploration and analysis from temporal, spatial, and spatiotemporal perspectives. VolumeSTCube is evaluated through a computational experiment, a real-world case study with one expert, and a controlled user study with twelve non-experts, compared against a baseline from prior work, showing its superiority and effectiveness in large-scale spatial time series analysis.

The in-network caching mechanism is one of the core technologies of the Content Centric Network (CCN) and has been increasingly concerned. In order to improve the cache hit ratio of the content centric network cache system and increase the content diversity of the cache system, this paper proposes a cooperative caching strategy based on content popularity and node centrality, called CCPNC. The CCPNC caching strategy comprehensively considers content popularity and node distribution rules. It can separately cache content objects based on different popularity and mobilize the core routing nodes in the network to work together with non-core routing nodes. The CCPNC caching strategy not only makes use of the core routing node cache resources to provide faster popular content services for a wide range of users, but also avoids unnecessary high-frequency cache replacement of the core routing nodes. Meanwhile, it utilizes the cache resources of non-core routing nodes to provide more convenient non-popular content services. Through simulation experiments, it is found that the CCPNC caching strategy can effectively balance the distribution of content objects in the cache system and improve the cache hit ratio of the content centric network, while reducing the average routing hop and average request latency of content backhaul.