Non-pharmaceutical interventions against COVID-19 and other infectious diseases seek good trade-offs between reducing the number of infections and their socioeconomic costs. We propose a framework that establishes these costs from data on interventions implemented in real life for each country taking into consideration its culture and economy. The study used data from 235 territories, and presents detailed results on 17 developed countries with high-quality data. We find that these countries selected substantially different cost-benefit trade-offs. They also differed significantly in how much unnecessary cost, which is the cost that could be avoided by better intervention policy without an increase in infections, that they incurred in doing so. We also analyzed the interplay between COVID-19 mortality, total and unnecessary costs, and the contribution of individual interventions to unnecessary costs. We concluded that the proposed framework for computational intervention planning could contribute to a more cost-effective pandemic management.
Today's fast paced industrial production requires automation at multiple steps during its process. Involving humans during the quality control inspection provides high degree of confidence that the end products are with the best quality. Workers involved in the control process may have an impact on production capacity by lowering the throughput, depending on the complexity of the control process at the time the control is carried out, during the process which is a time-critical operation, or after the process is completed. Companies are striving to fully automate their quality control stages of production and it comes naturally to focus on using various machine learning methods to help build a quality control pipeline which will offer high throughput and high degree of quality. In this paper we give an overview of applying several machine learning approaches in order to achieve an autonomous quality control pipeline. The applications for these approaches were used to help improve the quality control pipeline of two of the biggest manufacturing companies in Slovenia. One of the most challenging part of the study was that the tests had to be performed only on a small number of defective products, as is in reality. The motivation was to test several methods to find the most promising one for later actual application.Povzetek: Z nekaj prototipi je bila narejena analiza možnosti uporabe umetne inteligence v nekaj velikih slovenskih podjetjih.
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