Driving to a future without accidents? Connected automated vehicles impact on accident frequency and motor insurance risk

Road traffic accidents are largely driven by human error; therefore, the development of connected automated vehicles (CAV) is expected to signiicantly reduce accident risk. However, these changes are by no means proven and linear as diferent levels of automation show risk-related idiosyncrasies. A lack of empirical data aggravates the transparent evaluation of risk arising from CAVs with higher levels of automation capability. Nevertheless, it is likely that the risks associated with CAV will profoundly reshape the risk proile of the global motor insurance industry. This paper conducts a deep qualitative analysis of the impact of progressive vehicle automation and interconnectedness on the risks covered under motor third-party and comprehensive insurance policies. This analysis is enhanced by an assessment of potential emerging risks such as the risk of cyber-attacks. We ind that, in particular, primary insurers focusing on private retail motor insurance face signiicant strategic risks to their business model. The results of this analysis are not only relevant for insurance but also from a regulatory perspective as we ind a symbiotic relationship between an insurance-related assessment and a comprehensive evaluation of CAV’s inherent societal cost

Risk-adequate motor underwriting of automated vehicles: a qualitative evaluation using German focus groups

The advent of automated vehicles is already taking place and will significantly disrupt the motor insurance industry. The shift from the human driver to the system as the driver cannot be reflected in the current insurance risk assessment. This call for an amendment of the insurance underwriting was discussed with German experts from both the primary insurance and reinsurance sector with their professional background on motor insurance. Based on the findings, we propose an alternative method to underwrite automated vehicles of level 4 & 5 using an enhanced telematics approach which considers new risk categories such as systems used and the transformed role of the driver as the general user of the automated vehicle

Forecasting implied volatility in foreign exchange markets: a functional time series approach

We utilise novel functional time series (FTS) techniques to characterise and forecast implied volatility in foreign exchange markets. In particular, we examine the daily implied volatility curves of FX options, namely; Euro/United States Dollar, Euro/British Pound, and Euro/Japanese Yen. The FTS model is shown to produce both realistic and plausible implied volatility shapes that closely match empirical data during the volatile 2006-2013 period. Furthermore, the FTS model significantly outperforms implied volatility forecasts produced by traditionally employed parametric models. The evaluation is performed under both in-sample and out-of-sample testing frameworks with our findings shown to be robust across various currencies, moneyness segments, contract maturities, forecasting horizons, and out-of-sample window lengths. The economic significance of the results is highlighted through the implementation of a simple trading strategy

Outperformance in exchange-traded fund pricing deviations: generalized control of data snooping bias

An investigation into exchange-traded fund (ETF) outperforrnance during the period 2008-2012 is undertaken utilizing a data set of 288 U.S. traded securities. ETFs are tested for net asset value (NAV) premium, underlying index and market benchmark outperformance, with Sharpe, Treynor, and Sortino ratios employed as risk-adjusted performance measures. A key contribution is the application of an innovative generalized stepdown procedure in controlling for data snooping bias. We find that a large proportion of optimized replication and debt asset class ETFs display risk-adjusted premiums with energy and precious metals focused funds outperforming the S&P 500 market benchmark. (C) 2013 Elsevier B.V. All rights reserved

Reduction of health care-associated infections (HAIs) with antimicrobial inorganic nanoparticles incorporated in medical textiles: an economic assessment

Health care-associated infections (HAIs) affect millions of patients annually with up to 80,000 affected in Europe on any given day. This represents a significant societal and economic burden. Staff training, hand hygiene, patient identification and isolation and controlled antibiotic use are some of the standard ways to reduce HAI incidence but this is time consuming and subject and subject to rigorous implementation. In addition, the lack of antimicrobial activity of some disinfectants against healthcare-associated pathogens may also affect the efficacy of disinfection practices. Textiles are an attractive substrate for pathogens because of contact with the human body with the attendant warmth and moisture. Textiles and surfaces coated with engineered nanomaterials (ENMs) have shown considerable promise in reducing the microbial burden on those surfaces. Studies have also shown that this antimicrobial a ect can reduce the incidence of HAIs. For all of the promising research, there has been an absence of study on the economic effectiveness of ENM coated materials in a healthcare setting. This article examines the relative economic efficacy of ENM coated materials against an antiseptic approach. The goal is to establish the economic efficacy of the widespread usage of ENM coated materials in a healthcare setting. In the absence of detailed and segregated costs, benefits and control variables over at least cross sectional data or time series, an aggregated approach is warranted. This approach, while relying on some supposition allows for a comparison with similar data regarding standard treatment to reduce HAIs and provides a reasonable economic comparison. We find that while, relative to antiseptics, ENM coated textiles represent a significant clinical advantage, they can also offer considerable cost savings

Spatial risk modelling of behavioural hotspots: risk-aware path planning for autonomous vehicles

Autonomous vehicles (AVs) are expected to considerably improve road safety. That said, accident risk will continue to inflict societal costs. The ability to manage and measure these risks is fundamental to ensure societal acceptance and public adoption of AVs. In particular, the ability to quantitatively compare the safety of AVs relative to human drivers is crucial. Managing risk exposures through driving operational design domains (ODD) will also become prevalent. Ultimately, the deployment of AVs will hinge on the premise that they are safer than humans. In this paper, we posit a methodology to quantitatively evaluate AV risks and minimise their risk exposure once they are publically available. Two contributions are offered. First, we provide a proactive means of evaluating AV risks based on driving behaviour and safety-critical events. This offers statistically meaningful comparisons between humans and AVs given the limitation of current historical data. Second, we propose a novel risk-aware path planning methodology for AVs based on telematics behavioural data. Driving data from a cohort of young human drivers over roughly 270,000 km in Ireland is used to demonstrate the posited methodology. An unsupervised geostatistical tool called Kernel Density Estimation (KDE) is used to identify â behavioural hotspotsâ and the risk exposure at each edge or road segment is modelled. The results are incorporated into a path planning algorithm to find safe route paths for AVs, minimising risk exposures. In addition, Self-Organising Maps (SOM) are employed to identify similar risk groups and individual spatial risk patterns are considered

The toxic truth about carbon nanotubes in water purification: a perspective view

Without nanosafety guidelines, the long-term sustainability of carbon nanotubes (CNTs) for water purifications is questionable. Current risk measurements of CNTs are overshadowed by uncertainties. New risks associated with CNTs are evolving through different waste water purification routes, and there are knowledge gaps in the risk assessment of CNTs based on their physical properties. Although scientific efforts to design risk estimates are evolving, there remains a paucity of knowledge on the unknown health risks of CNTs. The absence of universal CNT safety guidelines is a specific hindrance. In this paper, we close these gaps and suggested several new risk analysis roots and framework extrapolations from CNT-based water purification technologies. We propose a CNT safety clock that will help assess risk appraisal and management. We suggest that this could form the basis of an acceptable CNT safety guideline. We pay particular emphasis on measuring risks based on CNT physico-chemical properties such as diameter, length, aspect ratio, type, charge, hydrophobicity, functionalities and so on which determine CNT behaviour in waste water treatment plants and subsequent release into the environment

Connected and autonomous vehicles: a cyber-risk classification framework

The proliferation of technologies embedded in connected and autonomous vehicles (CAVs) increases the potential of cyber-attacks. The communication systems between vehicles and infrastructure present remote attack access for malicious hackers to exploit system vulnerabilities. Increased connectivity combined with autonomous driving functions pose a considerable threat to the vast socioeconomic benefits promised by CAVs. However, the absence of historical information on cyber-attacks mean that traditional risk assessment methods are rendered ineffective. This paper proposes a proactive CAV cyber-risk classification model which overcomes this issue by incorporating known software vulnerabilities contained within the US National Vulnerability Database into model building and testing phases. This method uses a Bayesian Network (BN) model, premised on the variables and causal relationships derived from the Common Vulnerability Scoring Scheme (CVSS), to represent the probabilistic structure and parameterisation of CAV cyber-risk. The resulting BN model is validated with an out-of-sample test demonstrating nearly 100% prediction accuracy of the quantitative risk score and qualitative risk level. The model is then applied to the use-case of GPS systems of a CAV with and without cryptographic authentication. In the use case, we demonstrate how the model can be used to predict the effect of risk reduction measures

A machine learning tool to predict the antibacterial capacity of nanoparticles

The emergence and rapid spread of multidrug-resistant bacteria strains are a public health concern. This emergence is caused by the overuse and misuse of antibiotics leading to the evolution of antibiotic-resistant strains. Nanoparticles (NPs) are objects with all three external dimensions in the nanoscale that varies from 1 to 100 nm. Research on NPs with enhanced antimicrobial activity as alternatives to antibiotics has grown due to the increased incidence of nosocomial and community acquired infections caused by pathogens. Machine learning (ML) tools have been used in the field of nanoinformatics with promising results. As a consequence of evident achievements on a wide range of predictive tasks, ML techniques are attracting significant interest across a variety of stakeholders. In this article, we present an ML tool that successfully predicts the antibacterial capacity of NPs while the model’s validation demonstrates encouraging results (R2 = 0.78). The data were compiled after a literature review of 60 articles and consist of key physico-chemical (p-chem) properties and experimental conditions (exposure variables and bacterial clustering) from in vitro studies. Following data homogenization and pre-processing, we trained various regression algorithms and we validated them using diverse performance metrics. Finally, an important attribute evaluation, which ranks the attributes that are most important in predicting the outcome, was performed. The attribute importance revealed that NP core size, the exposure dose, and the species of bacterium are key variables in predicting the antibacterial effect of NPs. This tool assists various stakeholders and scientists in predicting the antibacterial effects of NPs based on their p-chem properties and diverse exposure settings. This concept also aids the safe-by-design paradigm by incorporating functionality tools

A supervised machine-learning prediction of textile’s antimicrobial capacity coated with nanomaterials

Textile materials, due to their large surface area and moisture retention capacity, allow the growth of microorganisms, causing undesired effects on the textile and on the end-users. The textile industry employs nanomaterials (NMs)/composites and nanofibers to enhance textile features such as water/dirt-repellent, conductivity, antistatic properties, and enhanced antimicrobial properties. As a result, textiles with antimicrobial properties are an area of interest to both manufacturers and researchers. In this study, we present novel regression models that predict the antimicrobial activity of nano-textiles after several washes. Data were compiled following a literature review, and variables related to the final product, such as the experimental conditions of nano-coating (finishing technologies) and the type of fabric, the physicochemical (p-chem) properties of NMs, and exposure variables, were extracted manually. The random forest model successfully predicted the antimicrobial activity with encouraging results of up to 70% coefficient of determination. Attribute importance analysis revealed that the type of NM, shape, and method of application are the primary features affecting the antimicrobial capacity prediction. This tool helps scientists to predict the antimicrobial activity of nano-textiles based on p-chem properties and experimental conditions. In addition, the tool can be a helpful part of a wider framework, such as the prediction of products functionality embedded into a safe by design paradigm, where products’ toxicity is minimized, and functionality is maximized