The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information.
Estimating voltage sag performance is important for distribution network operators who are keen to reduce costly interruptions, plan network investment and reduce operational expenditure. This paper proposes a robust method to locate faults and estimate the magnitude of voltage sags using information from a limited set of arbitrarily accurate monitoring devices. The developed method uses statistical analysis and impedance based fault location equations to find the most likely fault location and sag magnitude at non-monitored busbars. The method robustly handles measurement errors, and helps to eliminate some of the sensitivity present in existing impedance based fault location algorithms. The method is also shown to be effective at eliminating multiple fault location solutions caused by multiple overlapping impedance paths by synthesizing information from all monitors installed in a network. The method is validated and shown to be effective on a generic section of the UK's distribution network.
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