Robotic Railway Multi-Sensing and Profiling Unit Based on Artificial Intelligence and Data Fusion.

This article presents the research and results of field tests and simulations regarding an autonomous/robotic railway vehicle, designed to collect multiple information on safety and functional parameters of a surface railway and/or subway section, based on data fusion and machine learning. The maintenance of complex railways, or subway networks with long operating times is a difficult process and intensive resources consuming. The proposed solution delivers human operators in the fault management service and operations from the time-consuming task of railway inspection and measurements, by integrating several sensors and collecting most relevant information on railway, associated automation equipment and infrastructure on a single intelligent platform. The robotic cart integrates autonomy, remote sensing, artificial intelligence, and ability to detect even infrastructural anomalies. Moreover, via a future process of complex statistical filtering of data, it is foreseen that the solution might be configured to offer second-order information about infrastructure changes, such as land sliding, water flooding, or similar modifications. Results of simulations and field tests show the ability of the platform to integrate several fault management operations in a single process, useful in increasing railway capacity and resilience.

Data analysis evidence beyond correlation of a possible causal impact of weather on the COVID-19 spread, mediated by human mobility.

Previous correlative and modeling approaches indicate influences of environmental factors on COVID-19 spread through atmospheric conditions' impact on virus survival and transmission or host susceptibility. However, causal connections from environmental factors to the pandemic, mediated by human mobility, received less attention. We use the technique of Convergent Cross Mapping to identify the causal connections, beyond correlation at the country level, between pairs of variables associated with weather conditions, human mobility, and the number of COVID-19 cases for 32 European states. Here, we present data-based evidence that the relatively reduced number of cases registered in Northern Europe is related to the causal impact of precipitation on people's decision to spend more time at home and that the relatively large number of cases observed in Southern Europe is linked to people's choice to spend time outdoors during warm days. We also emphasize the channels of the significant impact of the pandemic on human mobility. The weather-human mobility connections inferred here are relevant not only for COVID-19 spread but also for any other virus transmitted through human interactions. These results may help authorities and public health experts contain possible future waves of the COVID-19 pandemic or limit the threats of similar human-to-human transmitted viruses.

Large-scale sea ice-Surface temperature variability linked to Atlantic meridional overturning circulation.

Due to its involvement in numerous feedbacks, sea ice plays a crucial role not only for polar climate but also at global scale. We analyse state-of-the-art observed, reconstructed, and modelled sea-ice concentration (SIC) together with sea surface temperature (SST) to disentangle the influence of different forcing factors on the variability of these coupled fields. Canonical Correlation Analysis provides distinct pairs of coupled Arctic SIC-Atlantic SST variability which are linked to prominent oceanic and atmospheric modes of variability over the period 1854-2017. The first pair captures the behaviour of the Atlantic meridional overturning circulation (AMOC) while the third and can be associated with the North Atlantic Oscillation (NAO) in a physically consistent manner. The dominant global SIC-Atlantic SST coupled mode highlights the contrast between the responses of Arctic and Antarctic sea ice to changes in AMOC over the 1959-2021 period. Model results indicate that coupled SST-SIC patterns can be associated with changes in ocean circulation. We conclude that a correct representation of AMOC-induced coupled SST-SIC variability in climate models is essential to understand the past, present and future sea-ice evolution.

Direct observation of protein motion during the photochemical reaction cycle of bacteriorhodopsin.

Platinum-coated, conductive atomic force microscope cantilevers were used to deposit electrophoretically purple membranes from Halobacterium salinarum on the bottom part of the cantilevers. By illuminating the bacteriorhodopsin-containing purple membranes, the protein goes through its photochemical reaction cycle, during which a conformational change happens in the protein, changing its shape and size. The size change of the protein acts upon the cantilever by causing its deflection, which can be monitored by the detection system of the atomic force microscope. The shape of the signal, the action spectrum of the deflection amplitude, and the blue light inhibition of the deflection all prove that the origin of the signal is the conformational change arising in the bacteriorhodopsin during the photocycle. From the size of the signal, the magnitude of the protein motion could be estimated. Using polarized light, the orientation of the motion was determined, relative to the transition moment of the retinal.