No abstract

Epidemiological surveillance is an essential component of public health practice especially during infectious disease outbreaks. It is critical to offer transparent epidemiological information in a rigorous manner at different regional levels in countries for managing the outbreak situations. The objectives of this research are to better understand the information flow of COVID-19 health monitoring systems and to determine the data gaps of COVID-19 incidence at the national and provincial levels in Indonesia. COVID-19 information flow was researched using government websites at the national and various provincial levels. To find the disparities, we assessed the number of cases reported at both levels at the same time and displayed the absolute and relative differences. The findings revealed that out of a total of 34 provinces in Indonesia, data differences were seen in 25 (73.52%) provinces in terms of positive cases, 31 (91.18%) provinces in terms of cured cases, and 28 (82.35%) provinces of the number of deaths. Our results showed a pressing need for high-quality, transparent, and timely information. The integration of COVID-19 data in Indonesia has not been optimal, implying that the reported COVID-19 incidence rate may be biased or delayed. COVID-19 incidents must be better monitored to disrupt the disease’s transmission chain.

This study highlights the cause and effects of part defects in ABS-Based samples using an additive manufacturing process. The parameters that were investigated include build orientation, infill pattern, number of contours, airgap, road width and annealing as a post-processing parameter. Samples were made, and their compressive strength was tested. Additionally, the tested samples were investigated using optical microscopy and the classification of their defects was done. This study is unique in investigating the effect of stress relief annealing along with build process parameters. Furthermore, the various defects associated with compressive failure in additively manufactured artefacts were categorized and a cause and effect diagram was derived which would enable designers to predict the areas of failure of a part.

The paper presents an adaptive speed controller for pennanent magnet DC riwtor using an Artijicial Neural Network (A"). The development of an efJicient training algorithm is one of the key problems in designing such ANN. The output error vector of the Neural Network is usually used in training, instead of the actual process output error. Since the desired control action is usually unknown, the output error of the ANN controller is also unknown. A simple on-line training algorithm, which enables the neural Network to be trained with the actual output error of the controlled drive planl is used. The direction of the controlled plant output response is the only a priori knowledge needed. The ANN based controller is eflective, robust, and result in high performance permanent magnet DC motor drives.

No abstract