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Background: Despite substantial resources deployed to curb SARS-CoV-2 transmission, controlling the COVID-19 pandemic has been a major challenge. New variants of the virus are frequently emerging leading to new waves of infection and re-introduction of control measures. In this study, we assessed the effectiveness of containment strategies implemented in the early phase of the pandemic. Methods: Real-world data for COVID-19 cases was retrieved for the period Jan 1 to May 1, 2020 from a number of different sources, including PubMed, MEDLINE, Facebook, Epidemic Forecasting and Google Mobility Reports. We analyzed data for 18 countries/regions that deployed containment strategies such as travel restrictions, lockdowns, stay-at-home requests, school/public events closure, social distancing, and exposure history information management (digital contact tracing, DCT). Primary outcome measure was the change in the number of new cases over 30 days before and after deployment of a control measure. We also compared the effectiveness of centralized versus decentralized DCT. Time series data for COVID-19 were analyzed using Mann-Kendall (M-K) trend tests to investigate the impact of these measures on changes in the number of new cases. The rate of change in the number of new cases was compared using M-K z-values and Sen’s slope. Results: In spite of the widespread implementation of conventional strategies such as lockdowns, travel restrictions, social distancing, school closures, and stay-at-home requests, analysis revealed that these measures could not prevent the spread of the virus. However, countries which adopted DCT with centralized data storage were more likely to contain the spread. Conclusions: Centralized DCT was more effective in containing the spread of COVID-19. Early implementation of centralized DCT should be considered in future outbreaks. However, challenges such as public acceptance, data security and privacy concerns will need to be addressed.

New study finds cobalt-free batteries and recycling progress can significantly alleviate long-term cobalt supply risks, however a cobalt supply shortage appears inevitable in the short- to medium-term, even under the most technologically optimistic scenario.

Omnidirectional mobile manipulators (OMMs) have been widely used due to their high mobility and operating flexibility. However, since OMMs are complex nonlinear systems with uncertainties, the dynamic modeling and control are always challenging problems. Koopman operator theory provides a data-driven modeling method to construct explicit linear dynamic models for the original nonlinear systems, using only input-output data. It then allows to design control system based on well-established model-based linear control methods. This paper designs a Koopman operator based model predictive control (MPC) scheme for trajectory tracking control of an OMM. Firstly, using Koopman operator and extended dynamic mode decomposition method, an approximate high-dimensional linear dynamic explicit expression for the OMM system is obtained. Then MPC is employed to achieve tracking control based on the derived linear Koopman model. Finally, to show modeling accuracy for the OMM, the Koopman model is evaluated via both simulation and experimental tests. The control performances of the Koopman operator based MPC design are also verified in the simulation and experimental results.

The dynamic modeling and control of omni-directional mobile manipulators (OMM) are challenging since they are highly nonlinear, strongly coupled, and multi-input multi-output uncertainty systems. Koopman operator theory can provide an explicit linear dynamic model for OMM, only based on input-output data. However, the derived dynamic model usually has modeling errors and cannot capture external unknown disturbances. In this paper, a robust data-driven control scheme is designed and implemented for an OMM, based on Koopman operator and a disturbance observer. Firstly, a data-driven Koopman model of OMM is constructed, solely using the collected input-output data. Then a disturbance observer is designed to online estimate the inherent modeling error of the Koopman model as well as external disturbances. The controller is designed by combining linear MPC with feedback compensation of the estimated disturbances. Finally, both simulations and experimental tests are conducted to verify the effectiveness and robustness of the proposed control scheme.

Human action recognition algorithms have garnered significant research interest due to their vast potential for applications. Existing human behavior recognition algorithms primarily focus on recognizing general behaviors using a large number of datasets. However, in industrial applications, there are typically constraints such as limited sample sizes and high accuracy requirements, necessitating algorithmic improvements. This article proposes a graph convolution neural network model that combines prior knowledge supervision and attention mechanisms, designed to fulfill the specific action recognition requirements for workers installing solar panels. The model extracts prior knowledge from training data, improving the training effectiveness of action recognition models and enhancing the recognition reliability of special actions. The experimental results demonstrate that the method proposed in this paper surpasses traditional models in terms of recognizing solar panel installation actions accurately. The proposed method satisfies the need for highly accurate recognition of designated person behavior in industrial applications, showing promising application prospects.