Taxing thoughts: Ireland, tax competition and the cost of intellectual capital

This paper examines the impact of tax competition on the commodfication of ideas, and points towards a particular set of negative consequences that affect the developing world. As multinational business becomes increasingly independent of national borders, the power relationship between business and government has shifted from one in which governments imposed tax on business in return for the privilege of operating within its jurisdiction, to one in which governments distort their tax system to suit business, in the hope of enticing them to locate on their shores. The race to the bottom in terms of tax rates has been well-chronicled in studies such as Christensen et al (2004), and Murphy (2006) Countries which were successful at the first round of tax competition are now finding that tax rates alone will not hold the multinationals on which they have become so dependent. The economic growth associated with their earlier success has brought high operating and wage costs. Multinationals who have remained lightly rooted in the soil of these countries can easily move their manufacturing to cheaper, emerging economies, taking with them their coveted jobs and exports. In order to retain them, these first round winning countries are now encouraging multinationals to locate their research and development as well as their production facilities with them. They hope that this is a less mobile activity, less easily replicated in a developing country, and so will anchor the multinational firmly in their territory. In this new level of the tax competition game, incentives are given not only for gross production, but for the production of knowledge. As a consequence, knowledge itself becomes commodified, and intellectual capital widely defined and privatised. This means that ideas previously shared must now be bought, and products previously sold at a price determined by the local market may now only be sold if the market can support their original, patent-protected form. This paper tracks the development from the old to the new rules of tax competition, using the example of Ireland to illustrate the strategies adopted at each stage. The rational, self-serving response of multinationals is explored, and the immediate downstream effects for developing countries discussed. The writings of Michel Foucault are used to gain perspective on the idea of intellectual capital. Finally, the sustainability of the new form of tax competition is questioned, and some hypotheses are formed about the longterm consequences

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

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

The research aims to provide a deep dive into the increasing ethical questions and contexts relating to the insurance industry’s sophisticated use of big data analytics, AI, and machine learning to sustain and develop its products and services. While the commercial opportunities are clear, there are questions of what costs the commercial benefits have in terms of how data are used and if the use leads to further tensions regarding privacy, surveillance, and profiling

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

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

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 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. For this purpose, we created an updated, 55-item strong version of the Behaviour of Young Novice Drivers Scale (BYNDS), originally developed by Scott-Parker et al. in 2010. To make the new version of the BYNDS understandable for German young novice drivers, this research used a new method of translation in combination with extensive pre-testing. As a result, we identified possible threats for response errors such as retrospective formulated questions or double negations.Due the adjustment of the possible sources of error the presented version of the BYNDS is semantically and conceptually different from the original However,due to the application of the updated version of the BYNDS in a robust sample of 700 participants,this paper presents the first reliable and validated tool to measure novices risky driving behaviour in Germany. Moreover, it offers an updated and extended version of the BYNDS that allows practitioners but also researchers to broaden their understanding of young driver risk

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

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. This study aims to demonstrate the application of machine learning (ML) algorithms for predicting the antioxidant efficiency of NMs. We manually compiled a comprehensive dataset based on a literature review of 62 in vitro studies. We extracted NMs’ physico-chemical (P-chem) properties, the NMs’ synthesis technique and various experimental conditions as input features to predict the antioxidant efficiency measured by a 2,2- diphenyl-1-picrylhydrazyl (DPPH) assay. Following data pre-processing, various regression models were trained and validated. The random forest model showed the highest predictive performance reaching an R2 = 0.83. The attribute importance analysis revealed that the NM’s type, core-size and dosage are the most important attributes influencing the prediction. Our findings corroborate with those of the prior research landscape regarding the importance of P-chem characteristics. This study expands the application of ML in the nano-domain beyond safety-related outcomes by capturing the functional performance. Accordingly, this study has two objectives: (1) to develop a model to forecast the antioxidant efficiency of NMs to complement conventional in vitro assays and (2) to underline the lack of a comprehensive database and the scarcity of relevant data and/or data management practices in the nanotechnology field, especially with regards to functionality assessments.</p

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. Collision factors that influence the crash environment are combined with the injuries that were suffered in MVCs. The influence of delta-V is captured using a mediation analysis, whereby delta-V acts as the focal point between crash factors and injury outcome. The mediation approach adds to existing research by presenting a detailed view of the relationship between injury severity, delta-V and other collision factors. We find evidence of competitive mediation, wherein a collision factor’s positive association with injury severity is offset by a negative association with delta-V. Neglecting to include delta-V in our study would have let the factor’s association with injury severity go undiscovered. In addition, certain collision factors are found to be related to injury severity solely because of delta-V, while others are found to have a significant impact regardless of delta-V. Our results support the multitude of policy recommendations that promote seatbelt use and warn against alcohol-impaired driving, and support the proliferation of safety-enabled vehicles whose technology can mitigate the bodily damage associated with detrimental crash type