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Mr. Barry Sheehan
Department of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick V94 T9PX, Ireland

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0 Autonomous Vehicles
0 Insurance
0 Risk Management
0 Cybersecurity
0 Emerging risks

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Insurance
Risk Management
Autonomous Vehicles

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Journal article
Published: 07 July 2021 in Array
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From February 2020, both urban and rural Ireland witnessed the rapid proliferation of the COVID-19 disease throughout its counties. During this period, the national COVID-19 responses included stay-at-home directives issued by the state, subject to varying levels of enforcement. In this paper, we present a new method to assess and rank the causes of Ireland COVID-19 deaths as it relates to mobility activities within each county provided by Google while taking into consideration the epidemiological confirmed positive cases reported per county. We used a network structure and rank propagation modelling approach using Personalised PageRank to reveal the importance of each mobility category linked to cases and deaths. Then a novel feature-selection method using relative prominent factors finds important features related to each county's death. Finally, we clustered the counties based on features selected with the network results using a customised network clustering algorithm for the research problem. Our analysis reveals that the most important mobility trend categories that exhibit the strongest association to COVID-19 cases and deaths include retail and recreation and workplaces. This is the first time a network structure and rank propagation modelling approach has been used to link COVID-19 data to mobility patterns. The infection determinants landscape illustrated by the network results aligns soundly with county socio-economic and demographic features. The novel feature selection and clustering method presented clusters useful to policymakers, managers of the health sector, politicians and even sociologists. Finally, each county has a different impact on the national total.

ACS Style

Furxhi Irini; Arash Negahdari Kia; Darren Shannon; Tim Jannusch; Finbarr Murphy; Barry Sheehan. Associations between mobility patterns and COVID-19 deaths during the pandemic: A network structure and rank propagation modelling approach. Array 2021, 11, 100075 .

AMA Style

Furxhi Irini, Arash Negahdari Kia, Darren Shannon, Tim Jannusch, Finbarr Murphy, Barry Sheehan. Associations between mobility patterns and COVID-19 deaths during the pandemic: A network structure and rank propagation modelling approach. Array. 2021; 11 ():100075.

Chicago/Turabian Style

Furxhi Irini; Arash Negahdari Kia; Darren Shannon; Tim Jannusch; Finbarr Murphy; Barry Sheehan. 2021. "Associations between mobility patterns and COVID-19 deaths during the pandemic: A network structure and rank propagation modelling approach." Array 11, no. : 100075.

Review
Published: 18 May 2021 in Sensors
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A telematics device is a vehicle instrument that comes preinstalled by the vehicle manufacturer or can be added later. The device records information about driving behavior, including speed, acceleration, and turning force. When connected to vehicle computers, the device can also provide additional information regarding the mechanical usage and condition of the vehicle. All of this information can be transmitted to a central database via mobile networks. The information provided has led to new services such as Usage Based Insurance (UBI). A range of consultants, industry commentators and academics have produced an abundance of projections on how telematics information will allow the introduction of services from personalized insurance, bespoke entertainment and advertise and vehicle energy optimization, particularly for Electric Vehicles (EVs). In this paper we examine these potential services against a backdrop of nascent regulatory limitations and against the technical capacity of the devices. Using a case study approach, we examine three applications that can use telematics information. We find that the expectations of service providers will be significantly tempered by regulatory and technical hurdles. In our discussion we detail these limitations and suggest a more realistic rollout of ancillary services.

ACS Style

Kevin McDonnell; Finbarr Murphy; Barry Sheehan; Leandro Masello; German Castignani; Cian Ryan. Regulatory and Technical Constraints: An Overview of the Technical Possibilities and Regulatory Limitations of Vehicle Telematic Data. Sensors 2021, 21, 3517 .

AMA Style

Kevin McDonnell, Finbarr Murphy, Barry Sheehan, Leandro Masello, German Castignani, Cian Ryan. Regulatory and Technical Constraints: An Overview of the Technical Possibilities and Regulatory Limitations of Vehicle Telematic Data. Sensors. 2021; 21 (10):3517.

Chicago/Turabian Style

Kevin McDonnell; Finbarr Murphy; Barry Sheehan; Leandro Masello; German Castignani; Cian Ryan. 2021. "Regulatory and Technical Constraints: An Overview of the Technical Possibilities and Regulatory Limitations of Vehicle Telematic Data." Sensors 21, no. 10: 3517.

Research article
Published: 23 March 2021 in Journal of Risk Research
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Cyber-attacks pose a growing threat to global commerce that is increasingly reliant on digital technology to conduct business. Traditional risk assessment and underwriting practices face serious shortcomings when encountered with cyber threats. Conventional assessment frameworks rate risk based on historical frequency and severity of losses incurred, this method is effective for known risks; however, due to the absence of historical data, prove ineffective for assessing cyber risk. This paper proposes a conceptual cyber risk classification and assessment framework, designed to demonstrate the significance of proactive and reactive barriers in reducing companies’ exposure to cyber risk and quantify the risk. This method combines a bow-tie model with a risk matrix to produce a rating based on the likelihood of a cyber-threat occurring and the potential severity of the resulting consequences. The model can accommodate both historical data and expert opinion and previously known frameworks to score the Threats, Barriers and Escalators for the framework. The resultant framework is applied to a large city hospital in Europe. The results highlighted both cyber weaknesses and actions that should be taken to bolster cyber defences. The results provide a quick visual guide that is assessable to both experts and management. It also provides a practical framework that allows insurers to assess risks, visualise areas of concern and record the effectiveness of implementing control barriers.

ACS Style

Barry Sheehan; Finbarr Murphy; Arash N. Kia; Ronan Kiely. A quantitative bow-tie cyber risk classification and assessment framework. Journal of Risk Research 2021, 1 -20.

AMA Style

Barry Sheehan, Finbarr Murphy, Arash N. Kia, Ronan Kiely. A quantitative bow-tie cyber risk classification and assessment framework. Journal of Risk Research. 2021; ():1-20.

Chicago/Turabian Style

Barry Sheehan; Finbarr Murphy; Arash N. Kia; Ronan Kiely. 2021. "A quantitative bow-tie cyber risk classification and assessment framework." Journal of Risk Research , no. : 1-20.

Journal article
Published: 18 February 2021 in Journal of Behavioral and Experimental Finance
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The closure of borders and traditional commerce due to the COVID-19 pandemic is expected to have a lasting financial impact. We determine whether the growth in COVID-19 affected index prices by examining equity markets in five regional epicentres, along with a ‘global’ index. We also investigate the impact of COVID-19 after controlling for investor sentiment, credit risk, liquidity risk, safe-haven asset demand and the price of oil. Despite controlling for these traditional market drivers, the daily totals of COVID-19 cases nevertheless explained index price changes in Spain, Italy, the United Kingdom and the United States. Similar results were not observed in China, the origin of the virus, nor in the ‘global’ index (MSCI World). Our results suggest that early interventions (China) and the spatiotemporal nature of pandemic epicentres (World) should be considered by governments, regulators and relevant stakeholders in the event of future COVID-19 ‘waves’ or further extreme societal disruptions.

ACS Style

Niall O’Donnell; Darren Shannon; Barry Sheehan. Immune or at-risk? Stock markets and the significance of the COVID-19 pandemic. Journal of Behavioral and Experimental Finance 2021, 30, 100477 -100477.

AMA Style

Niall O’Donnell, Darren Shannon, Barry Sheehan. Immune or at-risk? Stock markets and the significance of the COVID-19 pandemic. Journal of Behavioral and Experimental Finance. 2021; 30 ():100477-100477.

Chicago/Turabian Style

Niall O’Donnell; Darren Shannon; Barry Sheehan. 2021. "Immune or at-risk? Stock markets and the significance of the COVID-19 pandemic." Journal of Behavioral and Experimental Finance 30, no. : 100477-100477.

Article
Published: 29 May 2019 in Nanotoxicology
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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 in vitro characteristics with biological effects of NPs on a molecular cellular level from transcriptomics studies. The bridging is done by developing and implementing Bayesian Networks (BNs) with or without data preprocessing. The BN structures are derived either automatically or methodologically and compared. Early stage nanotoxicity measurements represent a challenge, not least when attempting to predict adverse outcomes and modeling is critical to understanding the biological effects of exposure to NPs. The preprocessed data-driven BN showed improved performance over automatically structured BN and the BN with unprocessed datasets. The prestructured BN captures inter relationships between NP properties, exposure condition and in vitro characteristics and links those with cellular effects based on statistic correlation findings. Information gain analysis showed that exposure dose, NP and cell line variables were the most influential attributes in predicting the biological effects. The BN methodology proposed in this study successfully predicts a number of toxicologically relevant cellular disrupted biological processes such as cell cycle and proliferation pathways, cell adhesion and extracellular matrix responses, DNA damage and repair mechanisms etc., with a success rate >80%. The model validation from independent data shows a robust and promising methodology for incorporating transcriptomics outcomes in a hazard and, by extension, risk assessment modeling framework by predicting affected cellular functions from experimental conditions.

ACS Style

Irini Furxhi; Finbarr Murphy; Craig Poland; Barry Sheehan; Martin Mullins; Paride Mantecca. Application of Bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics. Nanotoxicology 2019, 13, 827 -848.

AMA Style

Irini Furxhi, Finbarr Murphy, Craig Poland, Barry Sheehan, Martin Mullins, Paride Mantecca. Application of Bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics. Nanotoxicology. 2019; 13 (6):827-848.

Chicago/Turabian Style

Irini Furxhi; Finbarr Murphy; Craig Poland; Barry Sheehan; Martin Mullins; Paride Mantecca. 2019. "Application of Bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics." Nanotoxicology 13, no. 6: 827-848.

Journal article
Published: 08 November 2018 in Transportation Research Part A: Policy and Practice
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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.

ACS Style

Barry Sheehan; Finbarr Murphy; Martin Mullins; Cian Ryan. Connected and autonomous vehicles: A cyber-risk classification framework. Transportation Research Part A: Policy and Practice 2018, 124, 523 -536.

AMA Style

Barry Sheehan, Finbarr Murphy, Martin Mullins, Cian Ryan. Connected and autonomous vehicles: A cyber-risk classification framework. Transportation Research Part A: Policy and Practice. 2018; 124 ():523-536.

Chicago/Turabian Style

Barry Sheehan; Finbarr Murphy; Martin Mullins; Cian Ryan. 2018. "Connected and autonomous vehicles: A cyber-risk classification framework." Transportation Research Part A: Policy and Practice 124, no. : 523-536.

Journal article
Published: 25 February 2018 in International Journal of Molecular Sciences
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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. Two approaches have gained significant currency. Bayesian networks (BN) are a probabilistic, machine learning approach while the weight of evidence (WoE) statistical framework is based on expert elicitation. This comparative study investigates the efficacy of quantitative WoE and Bayesian methodologies in ranking the potential hazard of metal and metal-oxide NMs—TiO2, Ag, and ZnO. This research finds that hazard ranking is consistent for both risk assessment approaches. The BN and WoE models both utilize physico-chemical, toxicological, and study type data to infer the hazard potential. The BN exhibits more stability when the models are perturbed with new data. The BN has the significant advantage of self-learning with new data; however, this assumes all input data is equally valid. This research finds that a combination of WoE that would rank input data along with the BN is the optimal hazard assessment framework.

ACS Style

Barry Sheehan; Finbarr Murphy; Martin Mullins; Irini Furxhi; Anna L. Costa; Felice C. Simeone; Paride Mantecca. Hazard Screening Methods for Nanomaterials: A Comparative Study. International Journal of Molecular Sciences 2018, 19, 649 .

AMA Style

Barry Sheehan, Finbarr Murphy, Martin Mullins, Irini Furxhi, Anna L. Costa, Felice C. Simeone, Paride Mantecca. Hazard Screening Methods for Nanomaterials: A Comparative Study. International Journal of Molecular Sciences. 2018; 19 (3):649.

Chicago/Turabian Style

Barry Sheehan; Finbarr Murphy; Martin Mullins; Irini Furxhi; Anna L. Costa; Felice C. Simeone; Paride Mantecca. 2018. "Hazard Screening Methods for Nanomaterials: A Comparative Study." International Journal of Molecular Sciences 19, no. 3: 649.

Chapter
Published: 01 January 2018 in Development and Implementation of Health Technology Assessment
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Pillar One covers the available capital and the capital requirements which may be calculated by the pre-defined standard formula approach, by a partial internal model, or by a full internal model.

ACS Style

Maria Heep-Altiner; Martin Mullins; Torsten Rohlfs; Fabian Clasen; Gabriel Gallinger; Martin Gerlach; Valeria Keller; Andre Loeken; Harry Moor; Teresa Olbrich; Jakob Schwering; Barry Sheehan. Application of the Data Model: Pillar One. Development and Implementation of Health Technology Assessment 2018, 23 -84.

AMA Style

Maria Heep-Altiner, Martin Mullins, Torsten Rohlfs, Fabian Clasen, Gabriel Gallinger, Martin Gerlach, Valeria Keller, Andre Loeken, Harry Moor, Teresa Olbrich, Jakob Schwering, Barry Sheehan. Application of the Data Model: Pillar One. Development and Implementation of Health Technology Assessment. 2018; ():23-84.

Chicago/Turabian Style

Maria Heep-Altiner; Martin Mullins; Torsten Rohlfs; Fabian Clasen; Gabriel Gallinger; Martin Gerlach; Valeria Keller; Andre Loeken; Harry Moor; Teresa Olbrich; Jakob Schwering; Barry Sheehan. 2018. "Application of the Data Model: Pillar One." Development and Implementation of Health Technology Assessment , no. : 23-84.

Chapter
Published: 01 January 2018 in Development and Implementation of Health Technology Assessment
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Since January 2016, Solvency II has been integrated as the regulatory framework for the insurance industry with the objective of harmonising European Union (EU) insurance regulation. This framework has fundamentally reformed EU insurance supervisory law and bears little resemblance to its predecessor, Solvency I. In addition, the Solvency II regulations particularly value the functionality of companies’ governance and risk management systems in order to guarantee an effective and efficient control of the companies’ risks.

ACS Style

Maria Heep-Altiner; Martin Mullins; Torsten Rohlfs; Svenja Hintzen; Simon Muders; Barry Sheehan; Florian Vennemann. Introduction. Development and Implementation of Health Technology Assessment 2018, 1 -21.

AMA Style

Maria Heep-Altiner, Martin Mullins, Torsten Rohlfs, Svenja Hintzen, Simon Muders, Barry Sheehan, Florian Vennemann. Introduction. Development and Implementation of Health Technology Assessment. 2018; ():1-21.

Chicago/Turabian Style

Maria Heep-Altiner; Martin Mullins; Torsten Rohlfs; Svenja Hintzen; Simon Muders; Barry Sheehan; Florian Vennemann. 2018. "Introduction." Development and Implementation of Health Technology Assessment , no. : 1-21.

Journal article
Published: 01 September 2017 in Transportation Research Part C: Emerging Technologies
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ACS Style

Barry Sheehan; Finbarr Murphy; Cian Ryan; Martin Mullins; Hai Yue Liu. Semi-autonomous vehicle motor insurance: A Bayesian Network risk transfer approach. Transportation Research Part C: Emerging Technologies 2017, 82, 124 -137.

AMA Style

Barry Sheehan, Finbarr Murphy, Cian Ryan, Martin Mullins, Hai Yue Liu. Semi-autonomous vehicle motor insurance: A Bayesian Network risk transfer approach. Transportation Research Part C: Emerging Technologies. 2017; 82 ():124-137.

Chicago/Turabian Style

Barry Sheehan; Finbarr Murphy; Cian Ryan; Martin Mullins; Hai Yue Liu. 2017. "Semi-autonomous vehicle motor insurance: A Bayesian Network risk transfer approach." Transportation Research Part C: Emerging Technologies 82, no. : 124-137.

Original article
Published: 02 January 2017 in Nanotoxicology
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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., in vitro/in vivo data). The BN was validated with independent data extracted from published studies and the accuracy of the prediction of the nanomaterials hazard potential was 72% and for the biological effect 71%, respectively. The application of the BN is shown with scenario studies for TiO2, SiO2, Ag, CeO2, ZnO nanomaterials. It is demonstrated that the BN may be used by different stakeholders at several stages in the risk assessment to predict certain properties of a nanomaterials of which little information is available or to prioritize nanomaterials for further screening.

ACS Style

Hans J. P. Marvin; Yamine Bouzembrak; Esmée M. Janssen; Meike van der Zande; Finbarr Murphy; Barry Sheehan; Martin Mullins; Hans Bouwmeester. Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment. Nanotoxicology 2017, 11, 123 -133.

AMA Style

Hans J. P. Marvin, Yamine Bouzembrak, Esmée M. Janssen, Meike van der Zande, Finbarr Murphy, Barry Sheehan, Martin Mullins, Hans Bouwmeester. Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment. Nanotoxicology. 2017; 11 (1):123-133.

Chicago/Turabian Style

Hans J. P. Marvin; Yamine Bouzembrak; Esmée M. Janssen; Meike van der Zande; Finbarr Murphy; Barry Sheehan; Martin Mullins; Hans Bouwmeester. 2017. "Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment." Nanotoxicology 11, no. 1: 123-133.

Nano express
Published: 15 November 2016 in Nanoscale Research Letters
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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. This tool is tractable, accessible and scalable. Most importantly, it captures a broad span of data types, from complete, high quality data sets through to data sets with missing data and/or values with a relatively high spread of probability distribution. The tool is able to learn iteratively in order to further refine forecasts as the quality of data available improves. We demonstrate how this risk measurement approach works on NMs with varying degrees of risk potential, namely, carbon nanotubes, silver and titanium dioxide. The results afford even non-experts an accurate picture of the occupational risk probabilities associated with these NMs and, in doing so, demonstrated how NM risk can be evaluated into a tractable, quantitative risk comparator.

ACS Style

Finbarr Murphy; Barry Sheehan; Martin Mullins; Hans Bouwmeester; Hans J. P. Marvin; Yamine Bouzembrak; Anna Luisa Costa; Rasel Das; Vicki Stone; Syed A. M. Tofail. A Tractable Method for Measuring Nanomaterial Risk Using Bayesian Networks. Nanoscale Research Letters 2016, 11, 1 -8.

AMA Style

Finbarr Murphy, Barry Sheehan, Martin Mullins, Hans Bouwmeester, Hans J. P. Marvin, Yamine Bouzembrak, Anna Luisa Costa, Rasel Das, Vicki Stone, Syed A. M. Tofail. A Tractable Method for Measuring Nanomaterial Risk Using Bayesian Networks. Nanoscale Research Letters. 2016; 11 (1):1-8.

Chicago/Turabian Style

Finbarr Murphy; Barry Sheehan; Martin Mullins; Hans Bouwmeester; Hans J. P. Marvin; Yamine Bouzembrak; Anna Luisa Costa; Rasel Das; Vicki Stone; Syed A. M. Tofail. 2016. "A Tractable Method for Measuring Nanomaterial Risk Using Bayesian Networks." Nanoscale Research Letters 11, no. 1: 1-8.