Assessment of shallow landslide risk mitigation measures based on land use planning through probabilistic modelling

On October 25, 2011 an extreme rainfall event affected a wide area along the coasts of Cinque Terre (eastern Liguria, northern Italy). Particularly, in the Vernazza catchment, the event triggered hundreds of shallow landslides and a debris flood that caused three casualties. Investigation of slope stability after the event was carried out aiming at defining the most effective mitigation measures which may be adopted in future land use planning. To this objective a susceptibility model was produced and a series of scenarios were simulated using probabilistic methods. The susceptibility model has provided information about landslide conditioning factors on which to act for reducing landslide occurrence and therefore the associated risk. The simulations have taken into consideration the following alternative types of mitigation measures: (1) restoration of abandoned terraces, (2) reforestation of abandoned terraces, (3) use of local structural measures over stretches of potentially unstable hillsides and (4) avoidance of any intervention. The advantages and the disadvantages of proposed mitigation measures for shallow landslide risk are discussed considering the results of the simulations and taking into account their complex interaction with environmental, historical, cultural and socio-economic aspects. The results show that the most effective mitigation strategy for reducing landslide risk at short-term consists of applying structural measures over potentially unstable slopes. However a long-term program promoting the development of agricultural practices on terraced slopes is necessary. In fact, the simulations indicate if no measures are applied to avoid the degradation of the terraced areas, landslide areal frequency would inevitably increase

Applicability of machine learning approaches for structural damage detection of offshore wind jacket structures based on low resolution data

Structural damage in offshore wind jacket support structures are relatively unlikely due to the precautions taken in design but it could imply dramatic consequences if undetected. This work explores the possibilities of damage detection when using low resolution data, which are available with lower costs compared to dedicated high-resolution structural health monitoring. Machine learning approaches showed to be generally feasible for detecting a structural damage based on SCADA data collected in a simulation environment. Focus is here given to investigate model uncertainties, to assess the applicability of machine learning approaches for reality. Two jacket models are utilised representing the as-designed and the as-installed system, respectively. Extensive semi-coupled simulations representing different operating load cases are conducted to generate a database of low-resolution signals serving the machine learning training and testing. The analysis shows the challenges of classification approaches, i.e. supervised learning aiming to separate healthy and damage status, in coping with the uncertainty in system dynamics. Contrarily, an unsupervised novelty detection approach shows promising results when trained with data from both, the as-designed and the as-installed system. The findings highlight the importance of investigating model uncertainties and careful selection of training data

Health related quality of life in COVID-19 survivors discharged from acute hospitals: results of a short-form 36-item survey [version 1; peer review: awaiting peer review]

Background: Health-related quality of life (HRQL) is important for evaluating the impact of a disease in the longer term across the physical and psychological domains of human functioning. The aim of this study is to evaluate HRQL in COVID-19 survivors in Italy using the short form 36-items questionnaire (SF-36). Methods: This is an observational study involving adults discharged home following a coronavirus disease 2019 (COVID-19)-related hospital admission. Baseline demographic and clinical data including the Cumulative Illness Rating Scale (CIRS) and the Hospital Anxiety and Depression Scale (HADS) were collected. The validated Italian version of SF-36 was administered cross-sectionally. The SF-36 contains eight scales measuring limitations in physical and social functioning, the impact on roles and activities, fatigue, emotional wellbeing, pain and general health perception. Results: A total of 35 patients, with a mean age of 60 years, completed the SF-36. The results showed difficulties across the physical and psychological domains, particularly affecting the return to previous roles and activities. A higher burden of co-morbidities as well as a more severe muscle weakness was associated to a lower physical functioning. Younger age, rather than older, correlated to a perceived greater limitation in physical functioning and vitality. Conclusions: COVID-19 survivors particularly the ones of working age may need support for resuming their premorbid level of functioning and returning to work

Reliability, availability, maintainability data review for the identification of trends in offshore wind energy applications

This work presents a comprehensive review and discussion of the identification of critical components of the currently installed and next generation of offshore wind turbines. A systematic review on the reliability, availability, and maintainability data of both offshore and onshore wind turbines is initially performed, collecting the results from 24 initiatives, at system and subsystem level. Due to the scarcity of data from the offshore wind industry, the analysis is complemented with the extensive experience from onshore structures. Trends based on the deployment parameters for the influence of design characteristics and environmental conditions on the onshore wind turbines' reliability and availability are first investigated. The estimation of the operational availability for a set of offshore wind farm scenarios allowed a comparison with the recently published performance statistics and the discussion of the integrity of the data available to date. The failure statistics of the systems deployed offshore are then discussed and compared to the onshore ones, with regard to their normalised results. The availability calculations supported the hypothesis of the negative impact of the offshore environmental conditions on the reliability figures. Nonetheless, similarities in the reliability figures of the blade adjustment system and the maintainability of the power generation and the control systems are outlined. Finally, to improve the performance prediction of future offshore projects, recommendations on the effort worth putting into research and data collection are provided

Feasibility of machine learning algorithms for classifying damaged offshore jacket structures using SCADA data

The best practise for structural damage detection currently relies on the installation of structural health monitoring systems for the collection of dedicated high frequency measurements. Switching to the employment of the wind turbine's SCADA (Supervisory Control and Data Acquisition) signals and their commonly recorded low frequency statistics can lead to a reduction in the number of ad-hoc monitoring sensors and quantity of data required. In this paper, aero-hydro-servo-elastic simulations for a model of a turbine are used to assess its loads and any changes in the dynamics under healthy state and a damaged configuration case study. To prove the feasibility of the damage detection through low-resolution data, the statistics of the typically recorded signals from the SCADA and the structural monitoring systems are fed into a database for training and testing of classification algorithms. The ability of the machine learning models to generalise the classification for both stochasticity and uncertainties in the environmental conditions are tested. Decision tree-based classifiers showed the capability to capture the damage for the majority of the operating conditions considered. Though the setup of the traditional SCADA sensors had to be supplemented with an additional structural health monitoring sensor, the detection of the damage has been shown feasible by referring to low-frequency statistics only

Racial and ethnic disparities in access to minimally invasive mitral valve surgery

IMPORTANCE: Whether people from racial and ethnic minority groups experience disparities in access to minimally invasive mitral valve surgery (MIMVS) is not known. OBJECTIVE: To investigate racial and ethnic disparities in the utilization of MIMVS. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used data from the Society of Thoracic Surgeons Database for patients who underwent mitral valve surgery between 2014 and 2019. Statistical analysis was performed from January 24 to August 11, 2022. EXPOSURES: Patients were categorized as non-Hispanic White, non-Hispanic Black, and Hispanic individuals. MAIN OUTCOMES AND MEASURES: The association between MIMVS (vs full sternotomy) and race and ethnicity were evaluated using logistic regression. RESULTS: Among the 103 753 patients undergoing mitral valve surgery (mean [SD] age, 62 [13] years; 47 886 female individuals [46.2%]), 10 404 (10.0%) were non-Hispanic Black individuals, 89 013 (85.8%) were non-Hispanic White individuals, and 4336 (4.2%) were Hispanic individuals. Non-Hispanic Black individuals were more likely to have Medicaid insurance (odds ratio [OR], 2.21; 95% CI, 1.64-2.98; P \u3c .001) and to receive care from a low-volume surgeon (OR, 4.45; 95% CI, 4.01-4.93; P \u3c .001) compared with non-Hispanic White individuals. Non-Hispanic Black individuals were less likely to undergo MIMVS (OR, 0.65; 95% CI, 0.58-0.73; P \u3c .001), whereas Hispanic individuals were not less likely to undergo MIMVS compared with non-Hispanic White individuals (OR, 1.08; 95% CI, 0.67-1.75; P = .74). Patients with commercial insurance had 2.35-fold higher odds of undergoing MIMVS (OR, 2.35; 95% CI, 2.06-2.68; P \u3c .001) than those with Medicaid insurance. Patients operated by very-high volume surgeons (300 or more cases) had 20.7-fold higher odds (OR, 20.70; 95% CI, 12.7-33.9; P \u3c .001) of undergoing MIMVS compared with patients treated by low-volume surgeons (less than 20 cases). After adjusting for patient risk, non-Hispanic Black individuals were still less likely to undergo MIMVS (adjusted OR [aOR], 0.88; 95% CI, 0.78-0.99; P = .04) and were more likely to die or experience a major complication (aOR, 1.25; 95% CI, 1.16-1.35; P \u3c .001) compared with non-Hispanic White individuals. CONCLUSIONS AND RELEVANCE: In this cross-sectional study, non-Hispanic Black patients were less likely to undergo MIMVS and more likely to die or experience a major complication than non-Hispanic White patients. These findings suggest that efforts to reduce inequity in cardiovascular medicine may need to include increasing access to private insurance and high-volume surgeons

Progress on the development of a holistic coupled model of dynamics for offshore wind farms : phase II - study on a data-driven based reduced-order model for a single wind turbine

At present, over 1500 offshore wind turbines (OWTs) are operating in the UK with a capacity of 5.4GW. Until now, the research has mainly focused on how to minimise the CAPEX, but Operation and Maintenance (O&M) can represent up to 39% of the lifetime costs of an offshore wind farm, mainly due to the assets’ high cost and the harsh environment in which they operate. Focusing on O&M, the HOME Offshore research project (www.homeoffshore.org) aims to derive an advanced interpretation of the fault mechanisms through holistic multiphysics modelling of the wind farm. With the present work, an advanced model of dynamics for a single wind turbine is developed, able to identify the couplings between aero-hydro-servo-elastic (AHSE) dynamics and drive train dynamics. The wind turbine mechanical components, modelled using an AHSE dynamic model, are coupled with a detailed representation of a variable-speed direct-drive 5MW permanent magnet synchronous generator (PMSG) and its fully rated voltage source converters (VSCs). Using the developed model for the wind turbine, several case studies are carried out for above and below rated operating conditions. Firstly, the response time histories of wind turbine degrees of freedom (DOFs) are modelled using a full-order coupled analysis. Subsequently, regression analysis is applied in order to correlate DOFs and generated rotor torque (target degree of freedom for the failure mode in analysis), quantifying the level of inherent coupling effects. Finally, the reduced-order multiphysics models for a single offshore wind turbine are derived based on the strength of the correlation coefficients. The accuracy of the proposed reduced-order models is discussed, comparing it against the full-order coupled model in terms of statistical data and spectrum. In terms of statistical results, all the reducedorder models have a good agreement with the full-order results. In terms of spectrum, all the reduced-order models have a good agreement with the full-order results if the frequencies of interest are below 0.75Hz