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1. A Flip‐book of Knot Diagrams for Visualizing Surfaces in 4‐Space

   https://doi.org/10.1111/cgf.14545  

   Liu, Huan; Zhang, Hui (August 2022, Computer Graphics Forum)

   Abstract Just as 2D shadows of 3D curves lose structure where lines cross, 3D graphics projections of smooth 4D topological surfaces are interrupted where one surface intersects itself. They twist, turn, and fold back on themselves, leaving important but hidden features behind the surface sheets. In this paper, we propose a smart slicing tool that can read the 4D surface in its entropy map and suggest the optimal way to generate cross‐sectional images — or “slices” — of the surface to visualize its underlying 4D structure. Our visualization thinks of a 4D‐embedded surface as a collection of 3D curves stacked in time, very much like a flip‐book animation, where successive terms in the sequence differ at most by a critical change. This novel method can generate topologically meaningful visualization to depict complex and unfamiliar 4D surfaces, with the minimum number of cross‐sectional diagrams. Our approach has been successfully used to create flip‐books of diagrams to visualize a range of known 4D surfaces. In this preliminary study, our results show that the new visualization and slicing tool can help the viewers to understand and describe the complex spatial relationships and overall structures of 4D surfaces. 

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2. 4D Printed Actuation with Spatially-Varying Lattices

   https://doi.org/10.26153/tsw/58032  

   Delgado_Ramos, Katia L; Aranzola, Ana Paola; Akbar, Ijaz; Cervantes, Aaron; Martínez, Ana C; Maurel, Alexis; MacDonald, Eric; Castellanos, Alejandra G; El_Mansori, Mohamed; Roberson, David A (January 2024, Texas ScholarWorks/ University of Texas Libraries)

   Creating 3D printed structures from materials with shape memory properties allows these structures to change form, modifying configuration or function over time in response to external stimuli such as temperature, light, electrical current, etc. This area of additive manufacturing has come to be known as 4D printing. A variety of geometries have been previously explored in the context of 4D printing, including foldable surfaces (e.g. Origami), lattices, and bio-inspired shapes. However, with advances in solid modeling software tools, more sophisticated spatially- varying lattices are now easily generated to further optimize the mechanical performance and functionality of a 4D printed structure. In this work, complex lattices are created to bend at specific locations with intentionally-reduced stiffness and improved compliance based on locally-reduced strut dimensions. By experimentally demonstrating more complex geometries in the study of 4D printing, new applications can be considered that were not previously possible, with tailored performance allowing for balancing between weight and actuation. 

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3. A hybrid adjacency and time-based data structure for analysis of temporal networks

   https://doi.org/10.1007/s41109-022-00489-5  

   Hilsabeck, Tanner; Arastuie, Makan; Xu, Kevin_S (July 2022, Applied Network Science)

   Abstract Dynamic or temporal networks enable representation of time-varying edges between nodes. Conventional adjacency-based data structures used for storing networks such as adjacency lists were designed without incorporating time and can thus quickly retrieve all edges between two sets of nodes (anode-based slice) but cannot quickly retrieve all edges that occur within a given time interval (atime-based slice). We propose a hybrid data structure for storing temporal networks that stores edges in both an adjacency dictionary, enabling rapid node-based slices, and an interval tree, enabling rapid time-based slices. Our hybrid structure also enablescompound slices, where one needs to slice both over nodes and time, either by slicing first over nodes or slicing first over time. We further propose an approach for predictive compound slicing, which attempts to predict whether a node-based or time-based compound slice is more efficient. We evaluate our hybrid data structure on many real temporal network data sets and find that they achieve much faster slice times than existing data structures with only a modest increase in creation time and memory usage. 

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4. OnSlicing: online end-to-end network slicing with reinforcement learning

   https://doi.org/10.1145/3485983.3494850  

   Liu, Qiang; Choi, Nakjung; Han, Tao (December 2021, Proceedings of the 17th International Conference on Emerging Networking EXperiments and Technologies)

   Network slicing allows mobile network operators to virtualize infrastructures and provide customized slices for supporting various use cases with heterogeneous requirements. Online deep reinforcement learning (DRL) has shown promising potential in solving network problems and eliminating the simulation-to-reality discrepancy. Optimizing cross-domain resources with online DRL is, however, challenging, as the random exploration of DRL violates the service level agreement (SLA) of slices and resource constraints of infrastructures. In this paper, we propose OnSlicing, an online end-to-end network slicing system, to achieve minimal resource usage while satisfying slices' SLA. OnSlicing allows individualized learning for each slice and maintains its SLA by using a novel constraint-aware policy update method and proactive baseline switching mechanism. OnSlicing complies with resource constraints of infrastructures by using a unique design of action modification in slices and parameter coordination in infrastructures. OnSlicing further mitigates the poor performance of online learning during the early learning stage by offline imitating a rule-based solution. Besides, we design four new domain managers to enable dynamic resource configuration in radio access, transport, core, and edge networks, respectively, at a timescale of subseconds. We implement OnSlicing on an end-to-end slicing testbed designed based on OpenAirInterface with both 4G LTE and 5G NR, OpenDayLight SDN platform, and OpenAir-CN core network. The experimental results show that OnSlicing achieves 61.3% usage reduction as compared to the rule-based solution and maintains nearly zero violation (0.06%) throughout the online learning phase. As online learning is converged, OnSlicing reduces 12.5% usage without any violations as compared to the state-of-the-art online DRL solution. 

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5. Methods for Evaluating the Effects of 2D and 3D Culture Environment on Macrophage Response to Mycobacterium Infection

   https://doi.org/10.3390/microorganisms13092026  

   Stolley, Danielle L; Rasaputra, Komal S; May, Elebeoba E (September 2025, Microorganisms)

   Macrophages are critical to the formation of infection- and non-infection-associated immune structures such as cancer spheroids, pathogen-, and non-pathogen-associated granulomas, contributing to the spatiotemporal and chemical immune response and eventual outcome of disease. While well established in cancer immunology, the prevalence of using three-dimensional (3D) cultures to characterize later-stage structural immune response in pathogen-associated granulomas continues to increase, generating valuable insights for empirical and computational analysis. To enable integration of data from 3D in vitro studies with the vast bibliome of standard two-dimensional (2D) tissue culture data, methods that determine concordance between 2D and 3D immune response need to be established. Focusing on macrophage migration and oxidative species production, we develop experimental and computational methods to enable concurrent spatiotemporal and biochemical characterization of 2D versus 3D macrophage–mycobacterium interaction. We integrate standard biological sampling methods, time-lapse confocal imaging, and 4D quantitative image analysis to develop a 3D ex vivo model of Mycobacterium smegmatis infection using bone-marrow-derived macrophages (BMDMs) embedded in reconstituted basement membrane (RBM). Comparing features of 2D to 3D macrophage response that contribute to control and resolution of bacteria infection, we determined that macrophages in 3D environments increased production of reactive species, motility, and differed in cellular volume. Results demonstrate a viable and extensible approach for comparison of 2D and 3D datasets and concurrent biochemical plus spatiotemporal characterization of initial macrophage structural response during infection. 

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