The use of the polymerase chain reaction (PCR) in molecular diagnosis is now accepted worldwide and has become an essential tool in the research laboratory. In the laboratory, a rapid detection, serotyping and quantitation, one-step real-time RT-PCR assay was developed for dengue virus using TaqMan probes. In this assay, a set of forward and reverse primers were designed targeting the serotype conserved region at the NS5 gene, at the same time flanking a variable region for all four serotypes which were used to design the serotype-specific TaqMan probes. This multiplex one-step RT-PCR assay was evaluated using 376 samples collected during the year 2003. These groups included RNA from prototype dengue virus (1-4), RNA from acute serum from which dengue virus was isolated, RNA from tissue culture supernatants of dengue virus isolated, RNA from seronegative acute samples (which were culture and IgM negative) and RNA from samples of dengue IgM positive sera. The specificity of this assay was also evaluated using a panel of sera which were positive for other common tropical disease agents including herpes simplex virus, cytomegalovirus, measles virus, varicella-zoster virus, rubella virus, mumps virus, WWF, West Nile virus, Japanese encephalitis virus, S. typhi, Legionella, Leptospira, Chlamydia, and Mycoplasma. The sensitivity, specificity and real-time PCR efficiency of this assay were 89.54%, 100% and 91.5%, respectively.
Preventive maintenance activities require a tool to be offline for long hour in order to perform the prescribed maintenance activities. Although preventive maintenance is crucial to ensure operational reliability and efficiency of the tool, long hour of preventive maintenance activities increases the cycle time of the semiconductor fabrication foundry (Fab). Therefore, this activity is usually performed when the incoming Work-in-Progress to the equipment is forecasted to be low. The current statistical forecasting approach has low accuracy because it lacks the ability to capture the time-dependent behavior of the Work-in-Progress. In this paper, we present a forecasting model that utilizes machine learning method to forecast the incoming Work-In-Progress. Specifically, our proposed model uses LSTM to forecast multistep ahead incoming Work-in-Progress prediction to an equipment group. The proposed model's prediction results were compared with the results of the current statistical forecasting method of the Fab. The experimental results demonstrated that the proposed model performed better than the statistical forecasting method in both hit rate and Pearson's correlation coefficient, r.