We analyze the computational complexity of basic reconfiguration problems for the recently introduced surface Chemical Reaction Networks (sCRNs), where ordered pairs of adjacent species nondeterministically transform into a different ordered pair of species according to a predefined set of allowed transition rules (chemical reactions). In particular, two questions that are fundamental to the simulation of sCRNs are whether a given configuration of molecules can ever transform into another given configuration, and whether a given cell can ever contain a given species, given a set of transition rules. We show that these problems can be solved in polynomial time, are NP-complete, or are PSPACE-complete in a variety of different settings, including when adjacent species just swap instead of arbitrary transformation (swap sCRNs), and when cells can change species a limited number of times (k-burnout). Most problems turn out to be at least NP-hard except with very few distinct species (2 or 3).

We introduce a wait-free, multi-reader, single-writer, "kill -9" durable, indexing structure for in-memory social graph databases. This structure requires no communication from the readers back to the writer, allowing for trivial read scalability and isolation. We support online updates without compromising availability or read performance. Our structure supports looking up small subgraphs in 80 nanoseconds and a materialization rate of 12 nanoseconds per edge. Storage takes 7 bytes per edge per index and supports almost 1 million online writes per second.

As electronic records (e.g., medical records and technical defect records) accumulate, the retrieval of a record from a past instance with the same or similar circumstances, has become extremely valuable. This is because a past record may contain the correct diagnosis or correct solution to the current circumstance. We refer to the two records of the same or similar circumstances asmasterandduplicaterecords. Current record retrieval techniques are lacking when applied to this special master defect record retrieval problem. In this study, we propose a new paradigm for master defect record retrieval using network-based feature association (NBFA). We train the master record retrieval process by constructing feature associations to limit the search space. The retrieval paradigm was employed and tested on a real-world large-scale defect record database from a telecommunications company. The empirical results suggest that the NBFA was able to significantly improve the performance of master record retrieval, and should be implemented in practice. This paper presents an overview of technical aspects of the master defect record retrieval problem, describes general methodologies for retrieval of master defect records, proposes a new feature association paradigm, provides performance assessments on real data from a telecommunications company, and highlights difficulties and challenges in this line of research that should be addressed in the future.