Thermionic reactor power system: Effects of radiation on integration with Manned Space Station

The application of a thermionic reactor power system to the modular space station is described. The nominal net power is 40 kWe, with the power system designed to be applicable over the power range from 25 to 60 kWe. The power system is designed to be launched by the space shuttle. Radiation protection is provided by LiH neutron shielding and W gamma shielding in a shaped 4 pion configuration, i.e., the reactor is shielded on all sides but not to equal extent. Isodose contours are presented for the region around the modular space station. Levels and spectral distribution of radiation are given for later evaluation of effects on space station experiments. Parametric data on the effects of separation distance on power system mass are presented

Teachers’ Critical Challenges and Opportunities in Modular Distance Delivery

This paper explores the challenges and opportunities in Modular Distance Delivery. Data was collected from elementary schools of Bayugan City Division, Philippines. This research applied descriptive-quantitative-correlational method. The subject of this research are the Elementary teachers in Bayugan City Division Philippines. The study revealed that, support from the school in crafting modules for modular distance learning is very important to make an effective and efficient material for effective instructional delivery. It further explains that there is no significant difference among teachers’ critical challenges and opportunities in modular distance learning delivery. This means that the participants profile does not define their effectivity. No matter what the challenges in terms of modular distance learning delivery they do not back out they pass through it. The result of the study recommends to the DepEd School heads and administrators to support on crafting educational modules to create a well-crafted instructional material to address the needs of the learners especially in modular distance learning. Aside from that, teachers also ensure that the modules created suits to the needs of the learners

Chern-Simons Gauge Theory and the AdS(3)/CFT(2) Correspondence

The bulk partition function of pure Chern-Simons theory on a three-manifold is a state in the space of conformal blocks of the dual boundary RCFT, and therefore transforms non-trivially under the boundary modular group. In contrast the bulk partition function of AdS(3) string theory is the modular-invariant partition function of the dual CFT on the boundary. This is a puzzle because AdS(3) string theory formally reduces to pure Chern-Simons theory at long distances. We study this puzzle in the context of massive Chern-Simons theory. We show that the puzzle is resolved in this context by the appearance of a chiral "spectator boson" in the boundary CFT which restores modular invariance. It couples to the conformal metric but not to the gauge field on the boundary. Consequently, we find a generalization of the standard Chern-Simons/RCFT correspondence involving "nonholomorphic conformal blocks" and nonrational boundary CFTs. These generalizations appear in the long-distance limit of AdS(3) string theory, where the role of the spectator boson is played by other degrees of freedom in the theory.Comment: 43 pages, harvma

Some results on triangle partitions

We show that there exist efficient algorithms for the triangle packing problem in colored permutation graphs, complete multipartite graphs, distance-hereditary graphs, k-modular permutation graphs and complements of k-partite graphs (when k is fixed). We show that there is an efficient algorithm for C_4-packing on bipartite permutation graphs and we show that C_4-packing on bipartite graphs is NP-complete. We characterize the cobipartite graphs that have a triangle partition

Metrics for Graph Comparison: A Practitioner's Guide

Comparison of graph structure is a ubiquitous task in data analysis and machine learning, with diverse applications in fields such as neuroscience, cyber security, social network analysis, and bioinformatics, among others. Discovery and comparison of structures such as modular communities, rich clubs, hubs, and trees in data in these fields yields insight into the generative mechanisms and functional properties of the graph. Often, two graphs are compared via a pairwise distance measure, with a small distance indicating structural similarity and vice versa. Common choices include spectral distances (also known as $\lambda$ distances) and distances based on node affinities. However, there has of yet been no comparative study of the efficacy of these distance measures in discerning between common graph topologies and different structural scales. In this work, we compare commonly used graph metrics and distance measures, and demonstrate their ability to discern between common topological features found in both random graph models and empirical datasets. We put forward a multi-scale picture of graph structure, in which the effect of global and local structure upon the distance measures is considered. We make recommendations on the applicability of different distance measures to empirical graph data problem based on this multi-scale view. Finally, we introduce the Python library NetComp which implements the graph distances used in this work