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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.

This is a accompanied supplement to the study. First, a short presentation is given from a study on fracture diagnosis using electrical logging. In this study, substandard fracture fillers had a negative impact on the signal results. It was one of the motivations for this study. Second, we give the detailed form of the formula for the electrical properties of the mixture and a link to the literature. Third, we give a simulation test using coal coke fracture fillers, which mainly considers the mechanical properties of coal coke.

Dynamic nonlinear equations (DNEs) are essential for modeling complex systems in various fields due to their ability to capture real-world phenomena. However, the solution of DNEs presents significant challenges, especially in industrial settings where periodic noise often compromises solution fidelity. To tackle this challenge, we propose a novel approach called Periodic Noise Suppression Neural Dynamic (PNSND), which leverages the gradient descent approach and incorporates velocity compensation to overcome the limitations of the traditional Gradient Neural Dynamic (GND) model.

A well-functioning medical supply chain is essential for effective patient care and medical supply while preventing stockouts and delays, ensuring cost efficiency. To address these challenges, this paper presents a novel hybrid simulation-optimization methodology for optimizing pharmaceutical warehouse allocation, focusing on high-priority medicine flow and supply readiness.