COVID-19 was first reported in Wuhan, China, in December 2019. It is widely accepted that the world will not return to its prepandemic normality until safe and effective vaccines are available and a global vaccination program has been successfully implemented. Antisense RNAs are single-stranded RNAs that occur naturally or are synthetic and enable hybridizing and protein-blocking translation. Therefore, the main objective of this study was to identify target markers in the RNA of the severe acute respiratory syndrome coronavirus, or SARS-CoV-2, with a length between 21 and 28 bases that could enable the development of vaccines and therapies based on antisense RNA. We used a search algorithm in C language to compare 3159 complete nucleotide sequences from SARS-CoV-2 downloaded from the repository of the National Center for Biotechnology Information. The objective was to verify whether any common sequences were present in all 3159 strains of SARS-CoV-2. In the first of three datasets (SARS-CoV-2), the algorithm found two sequences each of 21 nucleotides (Sequence 1: CTACTGAAGCCTTTGAAAAAA; Sequence 2: TGTGGTTATACCTACTAAAAA). In the second dataset (SARS-CoV) and third dataset (MERS-CoV), no sequences of size N between 21 and 28 were found. Sequence 1 and Sequence 2 were input into BLAST® >> blastn and recognized by the platform. The gene identified by the sequences found by the algorithm was the ORF1ab region of SARS-CoV-2. Considerable progress in antisense RNA research has been made in recent years, and great achievements in the application of antisense RNA have been observed. However, many mechanisms of antisense RNA are not yet understood. Thus, more time and money must be invested into the development of therapies for gene regulation mediated by antisense RNA to treat COVID-19 as no effective therapy for this disease has yet been found.
This study presents an approach for fault detection and classification in a DC drive system. The fault is detected by a classical Luenberger observer. After the fault detection, the fault classification is started. The fault classification, the main contribution of this paper, is based on a representation which combines the Subctrative Clustering algorithm with an adaptation of Particle Swarm Clustering.
O pequi exerce um importante papel econômico e social no extrativismo norte mineiro, sendo explorado de forma ambientalmente sustentável. O seu valor socioeconômico pode ser observado da coleta ao consumo, sendo considerado, por isso, o ouro do cerrado, fonte de renda para inúmeras famílias. No entanto, identifica-se como um dos problemas da cadeia de extração do fruto a falta de logística para o escoamento da produção, o que gera um elevado custo e compromete a lucratividade dos extrativistas. Assim, o presente trabalho propõe uma logística de transporte para a exploração do pequi no Norte de Minas a partir da criação de uma modelagem no município de Japonvar/MG. Para o trabalho, foi feita uma revisão bibliográfica, visitas in loco, observação sistemática e tratamento das informações coletadas nas plataformas ArcGIS e TransCAD. A modelagem foi desenvolvida utilizando o Sistema de Informação Geográfico-SIG, que permitiu o mapeamento das áreas produtoras do município, a criação de facilidades para escoamento do fruto e o roteamento dos veículos com foco na minimização dos custos com o transporte. Como resultado, obtiveram-se as facilidades, a qualificação das vias de acesso às áreas produtoras e a roteirização dos veículos para a coleta.
The planning of forest production requires the adoption of mathematical models to optimize the utilization of available resources. Hence, studies involving the improvement of decision-making processes must be performed. Herein, we evaluate an alternative method for improving the performance of metaheuristics when they are applied for identifying solutions to problems in forest production planning. The inclusion of a solution obtained by rounding the optimal solution of linear programming to a relaxed problem is investigated. Such a solution is included in the initial population of the clonal selection algorithm, genetic algorithm, simulated annealing, and variable neighborhood search metaheuristics when it is used to generate harvest and planting plans in an area measuring 4,210 ha comprising 120 management units with ages varying between 1 and 6 years. The same algorithms are executed without including the solutions mentioned in the initial population. Results show that the performance of the clonal selection algorithm, genetic algorithm, and variable neighborhood search algorithms improved significantly. Positive effects on the performance of the simulated annealing metaheuristic are not indicated. Hence, it is concluded that rounding off the solution to a relaxed problem is a good alternative for generating an initial solution for metaheuristics.
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