MOTIVATION: Recent viral outbreaks motivate the systematic collection of pathogenic genomes in order to accelerate their study and monitor the apparition/spread of variants. Due to their limited length and temporal proximity of their sequencing, viral genomes are usually organized, and analyzed as oversized Multiple Sequence Alignments (MSAs). Such MSAs are largely ungapped, and mostly homogeneous on a column-wise level but not at a sequential level due to local variations, hindering the performances of sequential compression algorithms. RESULTS: In order to enable an efficient handling of MSAs, including subsequent statistical analyses, we introduce CREMSA (Column-wise Run-length Encoding for MSAs), a new index that builds on sparse bitvector representations to compress an existing or streamed MSA, all the while allowing for an expressive set of accelerated requests to query the alignment without prior decompression. Using CREMSA, a 65 GB MSA consisting of 1.9M SARS-CoV 2 genomes could be compressed into 22 MB using less than half a gigabyte of main memory, while executing access requests in the order of 100 ns. Such a speed up enables a comprehensive analysis of covariation over this very large MSA. We further assess the impact of the sequence ordering on the compressibility of MSAs and propose a resorting strategy that, despite the proven NP-hardness of an optimal sort, induces greatly increased compression ratios at a marginal computational cost. AVAILABILITY AND IMPLEMENTATION: CREMSA is freely accessible at https://gitlab.univ-lille.fr/cremsa/cremsa. The Snakemake workflow for the benchmarks is available at: https://gitlab.univ-lille.fr/cremsa/bench. The data used in the paper is on Zenodo at https://zenodo.org/records/14698859 and https://zenodo.org/records/15100011.

Tobacco industry companies need to conduct a regular inventory of finished products and raw and auxiliary materials, and drones with radio frequency identification (RFID) readers are becoming a major application trend of inventory. Under the condition of ensuring the accuracy of inventory, this article considers the physical performance constraints of the drone, the constraints of the RFID reader, etc., and introduces the force of the drone into the model establishment, and a task planning model for UAV inventory library equipped with RFID reader is proposed. Then, in view of the problem that the greedy strategy in the traditional differential evolution (DE) algorithm will cause the location information retained by other individuals to be lost, a hybrid DE algorithm based on the lion swarm optimization is proposed. Finally, the proposed algorithm was verified by environmental modeling based on the data of the tobacco enterprise warehouse.

In the context of Industry 5.0, in this article, we study the cyber–physical–social systems in unmanned aerial vehicles (UAVs) based on the task background of transmission line project acceptance. First, based on the idea of a parallel system, a parallel UAV planning system for transmission line project acceptance tasks is developed. The mathematical model based on swarm intelligence is then constructed from the multidimensional perspective of improving the acceptance efficiency, shortening the acceptance time, and reducing the energy consumption. This is done based on the physical system data while considering the constraints of UAVs in performing acceptance tasks. Experiments are simulated based on the actual environment data of the quality supervision and acceptance of a 500 kV transmission line project in a plateau area before it is put into production. The obtained results are compared with those of other similar cutting-edge algorithms. The efficiency and practicality of the proposed artificial parallel planning system are finally verified.

Since it is very difficult to compare linear cutting tool marks quickly and quantitatively using existing image‐processing and three‐dimensional scanning methods, an adaptive matching algorithm for laser detection signals of linear cutting marks is proposed. Using locally weighted scatterplot smoothing regression, the proposed algorithm first performs noise reduction on the surface signals of linear cutting tool marks that are detected by a laser displacement sensor. Trends of the thick and consistent features of the signal data are then identified and the feature vectors are quantised via cosine vector curve fitting to individually calculate the spatial distances of the samples. Finally, the most similar samples are matched via batch similarity comparison using a dynamic programming (DP) algorithm. The accuracy and validity of the proposed algorithm are verified by similarity comparison tests of actual cutting marks from a variety of samples.