Potato leafroll virus (PLRV) is a destructive virus of potatoes and responsible for high yield losses wherever potatoes are grown. In this study, DNA fragments containing ORF0 from each of nine PLRV isolates was sequenced. Sequence analysis data using 36 isolates from 12 different countries including 14 Iranian isolates showed that the identities of ORF0 at both nucleotide and amino acid levels between the Iranian isolates were 96-100 % and these isolates were more similar to the European PLRV isolates than to the other isolates. Furthermore, phylogenetic and population genetic analysis were carried out on the basis of full-length ORF0 and overlapping and non-overlapping regions of ORF0 and ORF1 (ORF0/1) which revealed that PLRV isolates were not geographically resolved. Also, we identified negative selection with different ratios for each of the mentioned genomic regions suggesting effects of F-box motif and -1 frameshift on ORF0 non-overlapping region and ORF0/1 in the selection pressure, respectively. Five recombination events were detected in the Iranian, Australian, and European isolates suggesting an important role for this phenomenon in influencing genetic diversity within this virus population.

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Accurate detection of possible machine failure allows manufacturers to identify potential fault situations in processes to avoid downtimes caused by unexpected tool wear or unacceptable workpiece quality. This paper aims to report the study of more than 20 fault detection models using Machine Learning (ML), Deep Learning (DL), and Deep Hybrid Learning (DHL). Predicting how the system could fail based on certain features or system settings (input variables) can help avoid future breakdowns and minimize downtime. The effectiveness of the proposed algorithms was experimented with a synthetic predictive maintenance dataset published by the School of Engineering of the University of Applied Sciences in Berlin, Germany. The fidelity of these algorithms was evaluated using performance measurement values such as accuracy, precision, recall, and the F-Score. Final results demonstrated that Deep Forest and Gradient Boosting algorithms had shown very high levels of average accuracy (exceeded 90%). Additionally, the Multinominal Logistic Regression and Long Short Term Memory based algorithms have shown satisfactory average accuracy (above 80%). Further analysis of models suggests that some models outperformed others. The research concluded that, through various ML, DL, and DHL algorithms, operational data analytics, and health monitoring system, engineers could optimize maintenance and reduce reliability risks.