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Understanding and quantifying the impact of severe weather events on the electric transmission and distribution system is crucial for ensuring its resilience in the context of the increasing frequency and intensity of extreme weather events caused by climate change. While weather impact models for the distribution system have been widely developed during the past decade, transmission system impact models lagged behind because of the scarcity of data. This study demonstrates a weather impact model for predicting the probability of failure of transmission lines. It builds upon a recently developed model and focuses on reducing model bias, through multi-model integration, feature engineering, and the development of a storm index that leverages distribution system data to aid the prediction of transmission risk. We explored three methods for integrating machine learning with mechanistic models. They consist of: (a) creating a linear combination of the outputs of the two modeling approaches, (b) including fragility curves as additional inputs to machine learning models, and (c) developing a new machine learning model that uses the outputs of the weather-based machine learning model, fragility curve estimates, and wind data to make new predictions. Moreover, due to the limited number of historical failures in transmission networks, a storm index was developed leveraging a dataset of distribution outages to learn about storm behavior to improve model skills. In the current version of the model, we substantially reduced the overestimation in the sum of predicted values of transmission line probability of failure that was present in the previously published model by a factor of 10. This has led to a reduction of model bias from 3352% to 14.46–15.43%. The model with the integrated approach and storm index demonstrates substantial improvements in the estimation of the probability of failure of transmission lines and their ranking by risk level. The improved model is able to capture 60% of the failures within the top 22.5% of the ranked power lines, compared to a value of 34.9% for the previous model. With an estimate of the probability of failure of transmission lines ahead of storms, power system planning and maintenance engineers will have critical information to make informed decisions, to create better mitigation plans and minimize power disruptions. Long term, this model can assist with resilience investments as it highlights areas of the system more susceptible to damage.

The complex interactions between the weather, the environment, and electrical infrastructure that result in power outages are not fully understood, but because of the threat of climate change, the need for models that describe how these factors produce power grid failures is acute. Without them, it remains difficult to understand the amount of weather-related damage we may expect in the future, as well as how changes or upgrades to the infrastructure may mitigate it. To address this problem, a modeling framework is proposed in this article that integrates data derived from structural vulnerability analysis into a machine-learning based weather-related power outage prediction model to create a model that is sensitive both to the weather and the technical configuration of the infrastructure. This Physics Informed Machine Learning (PIML) approach is demonstrated using data from a major power utility operating in the US State of Connecticut, and is compared against a fragility curve modeling approach using some of the same data. The validation of the PIML model shows superior predictive ability, as well as variable sensitivities that follow expected patterns. These results suggest that the model would be able to evaluate the influence that different configurations of the infrastructure would have on the occurrence of power outages caused by severe storms, allowing for the anticipated effects of investments in infrastructural upgrades to be quantified and optimized.

Smart contracts are self-executing programs running in the blockchain allowing for decentralised storage and execution without a middleman. On-chain execution is expensive, with miners charging fees for distributed execution according to a cost model defined in the protocol. In particular, transactions have a high fixed cost.We present MultiCall, a transaction-batching interpreter for Ethereum that reduces the cost of smart contract executions by gathering multiple users’ transactions into a batch. Our current implementation of MultiCall includes the following features: the ability to emulate Ethereum calls and create transactions, both from MultiCall itself and using an identity unique to the user; the ability to cheaply pay Ether to other MultiCall users; and the ability to authorise emulated transactions on behalf of multiple users in a single transaction using hash-based authorisation rather than more expensive signatures. This improves upon a previous version of MultiCall. Our experiments show that MultiCall provides a saving between 57% and 99% of the fixed transaction cost compared with the standard approach of sending Ethereum transactions directly.Besides, we also show how to prevent an economic attack exploiting the metatransaction feature, describe a generic protocol for hash-based authorisation of metatransactions, and analyse how to minimise its off-chain computational and storage cost.

Background:. Decubitus ulcers (DU) are a common pathology with significant morbidity and financial implications for health services globally. This study aimed to compare the burden of DU across European Union (EU) 15+ countries between 1990 and 2017. Methods:. Age-standardized incidence, mortality, and disability-adjusted life-years (DALYs) rates per 100,000 were extracted from the Global Burden of Disease Study online data repository for EU15+ countries (a group of 19 countries with comparable health expenditure, including the United States, Canada, the United Kingdom, and Australia). A joinpoint regression analysis was used to describe trends. Results:. The incidence of DU increased between 1990 and 2017 in 15 of 19 EU15+ countries for both men and women. Mortality from DU decreased over the time period analyzed in the majority of EU15+ countries: only in Denmark, Finland, and Germany were increasing mortality rates observed. Decreasing DALY rates were generally observed, with the largest decreases observed in Ireland for men and women. Denmark and Germany were the only countries to demonstrate unfavorable trends in mortality, incidence, and DALYs between 1990 and 2017 for men and women. The United States, the Netherlands, and France were the only EU15+ countries in which improving disease burden was identified between 1990 and 2017 for all parameters assessed. Conclusions:. Incidence of DU is increasing in EU15+ countries, whereas mortality rates and DALYs are improving. The trends in disease burden in Denmark and Germany have followed contrasting and unfavorable trends. Investigation into these trends is called for.

ObjectiveCurrent prevention and/or treatment options for respiratory syncytial virus (RSV) infections are limited as no vaccine is available. Prophylaxis with palivizumab is very expensive and requires multiple intramuscular injections over the RSV season. Here we present proof-of-concept data using nebulized palivizumab delivery as a promising new approach for the prevention or treatment of severe RSV infections, documenting both aerosol characteristics and pulmonary deposition patterns in the lungs of lambs.DesignProspective animal study.SettingBiosecurity Control Level 2-designated large animal research facility at the Murdoch Children’s Research Institute, Melbourne, Australia.SubjectsFour weaned Border-Leicester/Suffolk lambs at 5 months of age.InterventionsFour lambs were administered aerosolized palivizumab conjugated to Tc-99m, under gaseous anesthesia, using either the commercially available AeroNeb Go® or the investigational HYDRA device, placed in-line with the inspiratory limb of a breathing circuit. Lambs were scanned in a single-photon emission computed tomography (SPECT/CT) scanner in the supine position during the administration procedure.Measurements and Main ResultsBoth the HYDRA and AeroNeb Go® produced palivizumab aerosols in the 1–5 µm range with similar median (geometric standard deviation and range) aerosol droplet diameters for the HYDRA device (1.84 ± 1.40 μm, range = 0.54–5.41μm) and the AeroNeb Go® (3.07 ± 1.56 μm, range = 0.86–10 μm). Aerosolized palivizumab was delivered to the lungs at 88.79–94.13% of the total aerosolized amount for all lambs, with a small proportion localized to either the trachea or stomach. No difference between devices were found. Pulmonary deposition ranged from 6.57 to 9.25% of the total dose of palivizumab loaded in the devices, mostly in the central right lung.ConclusionsAerosolized palivizumab deposition patterns were similar in all lambs, suggesting a promising approach in the control of severe RSV lung infections.