Facing Erosion Identification in Railway Lines Using Pixel-wise Deep-based Approaches

Soil erosion is considered one of the most expensive natural hazards with a high impact on several infrastructure assets. Among them, railway lines are one of the most likely constructions for the appearance of erosion and, consequently, one of the most troublesome due to the maintenance costs, risks of derailments, and so on. Therefore, it is fundamental to identify and monitor erosion in railway lines to prevent major consequences. Currently, erosion identification is manually performed by humans using huge image sets, a time-consuming and slow task. Hence, automatic machine learning methods appear as an appealing alternative. A crucial step for automatic erosion identification is to create a good feature representation. Towards such objective, deep learning can learn data-driven features and classifiers. In this paper, we propose a novel deep learning-based framework capable of performing erosion identification in railway lines. Six techniques were evaluated and the best one, Dynamic Dilated ConvNet, was integrated into this framework that was then encapsulated into a new ArcGIS plugin to facilitate its use by non-programmer users. To analyze such techniques, we also propose a new dataset, composed of almost 2,000 high-resolution images

Dipeptidyl peptidase-1 inhibition in patients hospitalised with COVID-19: a multicentre, double-blind, randomised, parallel-group, placebo-controlled trial

Background Neutrophil serine proteases are involved in the pathogenesis of COVID-19 and increased serine protease activity has been reported in severe and fatal infection. We investigated whether brensocatib, an inhibitor of dipeptidyl peptidase-1 (DPP-1; an enzyme responsible for the activation of neutrophil serine proteases), would improve outcomes in patients hospitalised with COVID-19. Methods In a multicentre, double-blind, randomised, parallel-group, placebo-controlled trial, across 14 hospitals in the UK, patients aged 16 years and older who were hospitalised with COVID-19 and had at least one risk factor for severe disease were randomly assigned 1:1, within 96 h of hospital admission, to once-daily brensocatib 25 mg or placebo orally for 28 days. Patients were randomly assigned via a central web-based randomisation system (TruST). Randomisation was stratified by site and age (65 years or ≥65 years), and within each stratum, blocks were of random sizes of two, four, or six patients. Participants in both groups continued to receive other therapies required to manage their condition. Participants, study staff, and investigators were masked to the study assignment. The primary outcome was the 7-point WHO ordinal scale for clinical status at day 29 after random assignment. The intention-to-treat population included all patients who were randomly assigned and met the enrolment criteria. The safety population included all participants who received at least one dose of study medication. This study was registered with the ISRCTN registry, ISRCTN30564012. Findings Between June 5, 2020, and Jan 25, 2021, 406 patients were randomly assigned to brensocatib or placebo; 192 (47·3%) to the brensocatib group and 214 (52·7%) to the placebo group. Two participants were excluded after being randomly assigned in the brensocatib group (214 patients included in the placebo group and 190 included in the brensocatib group in the intention-to-treat population). Primary outcome data was unavailable for six patients (three in the brensocatib group and three in the placebo group). Patients in the brensocatib group had worse clinical status at day 29 after being randomly assigned than those in the placebo group (adjusted odds ratio 0·72 [95% CI 0·57–0·92]). Prespecified subgroup analyses of the primary outcome supported the primary results. 185 participants reported at least one adverse event; 99 (46%) in the placebo group and 86 (45%) in the brensocatib group. The most common adverse events were gastrointestinal disorders and infections. One death in the placebo group was judged as possibly related to study drug. Interpretation Brensocatib treatment did not improve clinical status at day 29 in patients hospitalised with COVID-19. Funding Sponsored by the University of Dundee and supported through an Investigator Initiated Research award from Insmed, Bridgewater, NJ; STOP-COVID19 trial

Integrating remote sensing and machine learning to detect turbidity anomalies in hydroelectric reservoirs

Monitoring water quality in reservoirs is essential for the maintenance of aquatic ecosystems and socioeconomic services. In this scenario, the observation of abrupt elevations of physicochemical parameters, such as turbidity and other indicators, can signal anomalies associated with the occurrence of critical events, requiring operational actions and planning to mitigate negative environmental impacts on water resources. This work aims to integrate Machine Learning methods specialized in anomaly detection with data obtained from remote sensing images to identify with high turbidity events in the surface water of the Três Marias Hydroelectric Reservoir. Four distinct threshold-based scenarios were evaluated, in which the overall performance, based on F1-score, showed decreasing trends as the thresholds became more restrictive. In general, the anomaly identification maps generated through the models ratified the applicability of the methods in the diagnosis of surface water in reservoirs in distinct hydrological contexts (dry and wet), effectively identifying locations with anomalous turbidity values