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Single-source shortest path (SSSP) discovery, one of a shortest path problem in algorithmic graph theory, is a combinatorial optimization problem. Most propositions solving the SSSP problem rely on Dijkstra’s algorithm. Although theoretically inferior in asymptotic upper bound time complexity, Dijkstra’s algorithm Binary variant outperforms Fibonacci variant empirically, in SSSP computations for real-world datasets, especially on sparse input graphs.

Dynamic models are indispensable for the optimization, prediction and control of thermal comfort in buildings.A new method is proposed for modeling the nonlinear dynamics of radiator-heated buildings and measuring modeluncertainties. The model uncertainty range obtained by the method can describe the uncertainties in the presence of model structure deviations and unknown noise distributions, without making restrictive assumptions that the noise follows a Gaussian distribution as in existing methods.

The dataset includes the Stanford Bunny, Elephant, and Pony models, which have been processed with added noise to adapt to the proposed algorithm. Additionally, the data encompasses point clouds of residential areas obtained by LIDAR, also subjected to noise addition. This comprehensive dataset, with its various models and point clouds, serves to thoroughly test and validate the robustness and effectiveness of the proposed algorithm in handling noisy data from different sources.

The noisy nanopore channel is introduced as a model of the nanopore sequencer in DNA storage that includes inter-symbol interference, sample duplications, and measurement noise. Information rates of the noisy nanopore channel with Markov sources are computed numerically based on a Monte Carlo technique that builds upon existing techniques for finite-state channels. However, the analogous technique for channels with duplications poses a challenging problem from an algorithmic perspective.