Employing Supervised Algorithms for the Prediction of Nanomaterial's Antioxidant Efficiency.

Reactive oxygen species (ROS) are compounds that readily transform into free radicals. Excessive exposure to ROS depletes antioxidant enzymes that protect cells, leading to oxidative stress and cellular damage. Nanomaterials (NMs) exhibit free radical scavenging efficiency representing a potential solution for oxidative stress-induced disorders.

(Re)Conceptualizing decision-making tools in a risk governance framework for emerging technologies-the case of nanomaterials.

The utility of decision-making tools for the risk governance of nanotechnology is at the core of this paper. Those working in nanotechnology risk management have been prolific in creating such tools, many derived from European FP7 and H2020-funded projects. What is less clear is how such tools might assist the overarching ambition of creating a fair system of risk governance.

The risk perception of nanotechnology: evidence from twitter.

Nanotechnology governance, particularly in relation to human and environmental concerns, remains a contested domain. In recent years, the creation of both a risk governance framework and council has been actively pursued. Part of the function of a governance framework is the communication to external stakeholders.

Cyber risk and cybersecurity: a systematic review of data availability.

Unlabelled: Cybercrime is estimated to have cost the global economy just under USD 1 trillion in 2020, indicating an increase of more than 50% since 2018. With the average cyber insurance claim rising from USD 145,000 in 2019 to USD 359,000 in 2020, there is a growing necessity for better cyber information sources, standardised databases, mandatory reporting and public awareness. This research analyses the extant academic and industry literature on cybersecurity and cyber risk management with a particular focus on data availability.

Creating ethics guidelines for artificial intelligence and big data analytics customers: The case of the consumer European insurance market.

The European Union (EU) has a strong reputation and track record for the development of guidelines for the ethical use of artificial intelligence (AI) generally. In this paper, we discuss the development of an AI and ethical framework by the European Insurance and Occupational Pensions Authority (EIOPA), for the European insurance market. EIOPA's earlier report on big data analytics (EIOPA, 2019) provided a foundation to analyze the complex range of issues associated with AI being deployed in insurance, such as behavioral insurance, parametric products, novel pricing and risk assessment algorithms, e-service, and claims management.

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.

A new version of the Behaviour of Young Novice Drivers Scale (BYNDS). Insights from a randomised sample of 700 German young novice drivers.

In Germany, every year 66,000 road crashes lead to death or injury of young novice drivers. This makes them twice as likely to be involved in, or cause, vehicle crashes compared to their older and more experienced counterparts. This study aims to address this societal issue by developing a better understanding of the German young driver problem.

Exploring the role of delta-V in influencing occupant injury severities - A mediation analysis approach to motor vehicle collisions.

This study investigates the impact that delta-V, the relative change in vehicle velocity pre- and post-crash, has on the severity of motor vehicle collisions (MVCs). We study injury severity using two metrics for each occupant - the number of injuries suffered, and the probability of suffering a serious or worse (MAIS 3+) injury. We use a cross-sectional set of generally-representative MVC data between 2010 and 2015 as a basis for our research.

Nanotoxicology data for tools: a literature review.

The exercise of non-testing approaches in nanoparticles (NPs) hazard assessment is necessary for the risk assessment, considering cost and time efficiency, to identify, assess, and classify potential risks. One strategy for investigating the toxicological properties of a variety of NPs is by means of computational tools that decode how nano-specific features relate to toxicity and enable its prediction. This literature review records systematically the data used in published studies that predict nano (eco)-toxicological endpoints using machine learning models.

Practices and Trends of Machine Learning Application in Nanotoxicology.

Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design.

Application of Bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics.

Inroads have been made in our understanding of the risks posed to human health and the environment by nanoparticles (NPs) but this area requires continuous research and monitoring. Machine learning techniques have been applied to nanotoxicology with very encouraging results. This study deals with bridging physicochemical properties of NPs, experimental exposure conditions and characteristics with biological effects of NPs on a molecular cellular level from transcriptomics studies.

Machine learning prediction of nanoparticle in vitro toxicity: A comparative study of classifiers and ensemble-classifiers using the Copeland Index.

Nano-Particles (NPs) are well established as important components across a broad range of products from cosmetics to electronics. Their utilization is increasing with their significant economic and societal potential yet to be fully realized. Inroads have been made in our understanding of the risks posed to human health and the environment by NPs but this area will require continuous research and monitoring.

Semiautonomous Vehicle Risk Analysis: A Telematics-Based Anomaly Detection Approach.

The transition to semiautonomous driving is set to considerably reduce road accident rates as human error is progressively removed from the driving task. Concurrently, autonomous capabilities will transform the transportation risk landscape and significantly disrupt the insurance industry. Semiautonomous vehicle (SAV) risks will begin to alternate between human error and technological susceptibilities.

Hazard Screening Methods for Nanomaterials: A Comparative Study.

Hazard identification is the key step in risk assessment and management of manufactured nanomaterials (NM). However, the rapid commercialisation of nano-enabled products continues to out-pace the development of a prudent risk management mechanism that is widely accepted by the scientific community and enforced by regulators. However, a growing body of academic literature is developing promising quantitative methods.

Applying crash data to injury claims - an investigation of determinant factors in severe motor vehicle accidents.

An extensive number of research studies have attempted to capture the factors that influence the severity of vehicle impacts. The high number of risks facing all traffic participants has led to a gradual increase in sophisticated data collection schemes linking crash characteristics to subsequent severity measures. This study serves as a departure from previous research by relating injuries suffered in road traffic accidents to expected trauma compensation payouts and deriving a quantitative cost function.

The Essential Elements of a Risk Governance Framework for Current and Future Nanotechnologies.

Societies worldwide are investing considerable resources into the safe development and use of nanomaterials. Although each of these protective efforts is crucial for governing the risks of nanomaterials, they are insufficient in isolation. What is missing is a more integrative governance approach that goes beyond legislation.

Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment.

In this study, a Bayesian Network (BN) was developed for the prediction of the hazard potential and biological effects with the focus on metal- and metal-oxide nanomaterials to support human health risk assessment. The developed BN captures the (inter) relationships between the exposure route, the nanomaterials physicochemical properties and the ultimate biological effects in a holistic manner and was based on international expert consultation and the scientific literature (e.g.

A Tractable Method for Measuring Nanomaterial Risk Using Bayesian Networks.

While control banding has been identified as a suitable framework for the evaluation and the determination of potential human health risks associated with exposure to nanomaterials (NMs), the approach currently lacks any implementation that enjoys widespread support. Large inconsistencies in characterisation data, toxicological measurements and exposure scenarios make it difficult to map and compare the risk associated with NMs based on physicochemical data, concentration and exposure route. Here we demonstrate the use of Bayesian networks as a reliable tool for NM risk estimation.

Empowering citizens in international governance of nanotechnologies.

The international dialogue on responsible governance of nanotechnologies engages a wide range of actors with conflicting as well as common interests. It is also characterised by a lack of evidence-based data on uncertain risks of in particular engineered nanomaterials. The present paper aims at deepening understanding of the collective decision making context at international level using the grounded theory approach as proposed by Glaser and Strauss in "The Discovery of Grounded Theory" (1967).

Impact and effectiveness of risk mitigation strategies on the insurability of nanomaterial production: evidences from industrial case studies.

Workers involved in producing nanomaterials or using nanomaterials in manufacturing plants are likely to have earlier and higher exposure to manufactured/engineered nanomaterials (ENM) than the general population. This is because both the volume handled and the probability of the effluence of 'free' nanoparticles from the handled volume are much higher during a production process than at any other stage in the lifecycle of nanomaterials and nanotechnology-enabled products. Risk assessment (RA) techniques using control banding (CB) as a framework for risk transfer represents a robust theory but further progress on implementing the model is required so that risk can be transferred to insurance companies.

Contemporary terrorism: risk perception in the London options market.

Previous studies have a demonstrated a linkage between terrorist attacks and the operation of financial markets. This article focuses on terrorist events carried out over the last five years and examines how they have been perceived among participants on the London financial market. Data from the London options market suggest a high degree of sensitivity to these events.