SUMMARY: Information regarding pathways through voids in biomolecules and their roles in ligand transport is critical to our understanding of the function of many biomolecules. Recently, the advent of high-throughput molecular dynamics simulations has enabled the study of these pathways, and of rare transport events. However, the scale and intricacy of the data produced requires dedicated tools in order to conduct analyses efficiently and without excessive demand on users. To fill this gap, we developed the TransportTools, which allows the investigation of pathways and their utilization across large, simulated datasets. TransportTools also facilitates the development of custom-made analyses. AVAILABILITY AND IMPLEMENTATION: TransportTools is implemented in Python3 and distributed as pip and conda packages. The source code is available at https://github.com/labbit-eu/transport_tools. Data are available in a repository and can be accessed via a link: https://doi.org/10.5281/zenodo.5642954. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Soft error is one of the major reliability issue with technology scaling. In this work, we propose a radiation hardened voltage bootstrapped schmitt trigger (VB-ST) latch. To evaluate the circuit radiation resilience, we calculated the critical charge under the PVT variations at the most sensitive node and observed that the proposed latch has the highest critical charge and the lowest soft error rate ratio when compared to existing latches. We analyzed the impact of process variations on our design and observed that the VB-ST latch has 0.42x less critical voltage variability as compared to ST latch. Further, dynamic power and propagation delay are examined for various supply voltages, and we observed that the VB-ST latch has the lowest power consumption and delay propagation when compared to the other considered latches. For the validation of the proposed latch, a charge to power-delay-area product ratio (QPAR) is calculated and we clearly observed that the proposed VB-ST based latch significantly outperforms the performance of existing designs.

This paper presents a new efficient method for implementing the Hilbert transform using an all-pass filter, based on fractional derivatives (FDs) and swarm optimization. In the proposed method, the squared error difference between the desired and designed responses of a filter is minimized. FDs are introduced to achieve higher accuracy at the reference frequency (ω0), which helps to reduce the overall phase error. In this paper, two approaches are used for finding the appropriate values of the FDs and reference frequencies. In the first approach, these values are estimated from a series of experiments, which require more computation time but produce less accurate results. These experiments, however, justify the behavior of the error function, with respect to the FD and ω0, as a multimodal and nonconvex problem. In the second approach, a variant of the swarm-intelligence-based multimodal search space technique, known as the constraint-factor particle swarm optimization, is exploited for finding the suitable values for the FD and ω0. The performance of the proposed FD-based method is measured in terms of fidelity aspects, such as the maximum phase error, total squared phase error, maximum group delay error, and total squared group delay error. The FD-based approach is found to reduce the total phase error by 57% by exploiting only two FDs.