Enterprise Ireland

Internet of Things (IoT) is considered one of the next industrial revolution enablers, which is fuelled by the advancement of digital technologies. IoT is dramatically changing how companies engage in business activities, and how people will interact with their environment. Its disruptive nature requires the assessment of the requirements for the future deployment across the digital value chain in various industries and in many application areas. The use of platforms is being driven by transformative technologies such as cloud, things, and mobile. The IoT and services makes it possible to create networks incorporating the entire manufacturing process that convert factories into a smart environment. The members of Alliance for Internet of Things Innovation jointly work on the creation of a dynamic European IoT ecosystem. This ecosystem is building on the work of the IoT Research Cluster and spill over innovation across industries and business sectors of IoT transforming ideas to IoT solutions.

Which companies should receive government R&D grants? Should R&D grants go to already dominant firms, whom one would expect to execute fully in innovation spending to produce better outcomes? Or, should grants go to firms that are less dominant and might need the funding for innovation? Using a novel proprietary panel dataset of over 26,000 firm‐year observations for Irish firms with 1,825 winning grants, this paper provides support for a strategic or efficiency effect: market dominance moderates grant innovation outcomes. Specifically, dominant firms significantly outperform other groups in terms of future R&D spending, R&D growth, and overall employment gains. However, they perform worse than other groups with respect to the employment of in‐house R&D workers, suggesting that dominant firms already have significant R&D capabilities. We also show that dominant firms’ future productivity is not statistically different from similar non‐grant backed dominant firms, potentially implying that they use grant money to retain their existing market position, rather than to capitalize on it and increase productivity by more than their peer firms.

ABSTRACT The COVID 19 pandemic has exposed businesses to high uncertainty through a number of channels. While the magnitude of this economic disruption is much larger than the 2008–2009 global financial crisis, there are pertinent learnings from the latter which can accelerate the recovery envisaged after the current crisis. To identify that, in this paper, we analyse the determinants of firms’ ability to maintain positive performance during an unexpected downturn. We do this by using a multinomial logit analysis on Italian automotive industry firm-level data covering the years before and after the 2008 financial crisis. The most significant results are that innovation and intangible assets play a key role in shaping the degree of firm ability to face external shocks. Our findings provide insights into solutions that have practical importance for companies pertaining to a mature industry like automotive. Learning from the economic downturn faced in 2008–2009, which we highlight in this study, can act as the first step into a green and sustainable recovery desired in the aftermath of the COVID 19 pandemic.

The article presents the results of research on the development of effective compositions of thermo-salt-resistant composite chemical reagents using local and secondary materials. Due to the high salinity of the reservoir water for the preparation of drilling fluids, water is brought from long distances and costs increase significantly. Based on the conducted research, we propose to introduce 3–20% (depending on the amount) of the KHR-R reagent to the composition of the drilling mud as an acid-soluble additive when opening fractured oil and gas reservoirs.

Improving the healing capacity of asphalt is proving to be an effective method to prolong the service life of an asphalt pavement. The calcium alginate capsules encapsulating rejuvenator have been developed and proved to provide successful localized crack healing in asphalt mastic. However, it is not known whether this self-healing asphalt system will improve healing capacity of a full asphalt mix. To this aim, this paper reports on study which investigate effect of the calcium alginate capsules onto self-healing capacity of the porous asphalt mix. X-ray computed tomography (XCT) was used to visualize the distribution of the capsules in porous asphalt. The effect of the capsules on fracture resistance of porous asphalt concrete was studied by semi-circular bending (SCB) tests. A semi-circular bending and healing programme was carried out to evaluate the healing effect of these capsules in porous asphalt concrete. Indirect Tensile Stiffness Modulus (ITSM) tests were employed in order to investigate the influence of the capsules on the stiffness of the porous asphalt concrete. The results indicate that incorporating calcium alginate capsules significantly improve the healing capacity of porous asphalt concrete without compromising its performance.

This study explored the use of artificial neural networks in the estimation of runners' kinetics from lower body kinematics. Three supervised feed-forward artificial neural networks with one hidden layer each were modelled and assigned individually with the mapping of a single force component. Number of training epochs, batch size and dropout rate were treated as modelling hyper-parameters and their values were optimised with a grid search. A public data set of twenty-eight professional athletes containing running trails of different speeds (2.5 m/sec, 3.5 m/sec and 4.5 m/sec) was employed to train and validate the networks. Movements of the lower limbs were captured with twelve motion capture cameras and an instrumented dual-belt treadmill. The acceleration of the shanks was fed to the artificial neural networks and the estimated forces were compared to the kinetic recordings of the instrumented treadmill. Root mean square error was used to evaluate the performance of the models. Predictions were accompanied with low errors: 0.134 BW for the vertical, 0.041 BW for the anteroposterior and 0.042 BW for the mediolateral component of the force. Vertical and anteroposterior estimates were independent of running speed (p=0.233 and p=.058, respectively), while mediolateral results were significantly more accurate for low running speeds (p=0.010). The maximum force mean error between measured and estimated values was found during the vertical active peak (0.114 ± 0.088 BW). Findings indicate that artificial neural networks in conjunction with accelerometry may be used to compute three-dimensional ground reaction forces in running.

The pressures on honeybee (Apis mellifera) populations, resulting from threats by modern pesticides, parasites, predators and diseases, have raised awareness of the economic importance and critical role this insect plays in agricultural societies across the globe. However, the association of humans with A. mellifera predates post-industrial-revolution agriculture, as evidenced by the widespread presence of ancient Egyptian bee iconography dating to the Old Kingdom (approximately 2400 BC). There are also indications of Stone Age people harvesting bee products; for example, honey hunting is interpreted from rock art in a prehistoric Holocene context and a beeswax find in a pre-agriculturalist site. However, when and where the regular association of A. mellifera with agriculturalists emerged is unknown. One of the major products of A. mellifera is beeswax, which is composed of a complex suite of lipids including n-alkanes, n-alkanoic acids and fatty acyl wax esters. The composition is highly constant as it is determined genetically through the insect's biochemistry. Thus, the chemical 'fingerprint' of beeswax provides a reliable basis for detecting this commodity in organic residues preserved at archaeological sites, which we now use to trace the exploitation by humans of A. mellifera temporally and spatially. Here we present secure identifications of beeswax in lipid residues preserved in pottery vessels of Neolithic Old World farmers. The geographical range of bee product exploitation is traced in Neolithic Europe, the Near East and North Africa, providing the palaeoecological range of honeybees during prehistory. Temporally, we demonstrate that bee products were exploited continuously, and probably extensively in some regions, at least from the seventh millennium cal BC, likely fulfilling a variety of technological and cultural functions. The close association of A. mellifera with Neolithic farming communities dates to the early onset of agriculture and may provide evidence for the beginnings of a domestication process.

Human computation systems are characterized by the use of human workers to solve computationally difficult problems. Expertise profiling involves assessment and representation of a worker’s expertise, in order to route human computation tasks to appropriate workers. This paper studies the relationship between the assessment workload on workers and the quality of task routing. Three expertise assessment approaches were compared with the help of a user study, using two different groups of human workers. The first approach requests workers to provide self-assessment of their knowledge. The second approach measures the knowledge of workers through their performance against tasks with known responses. We propose a third approach based on a combination of self-assessment and task-assessment. The results suggest that the self-assessment approach requires minimum assessment workload from workers during expertise profiling. By comparison, the task-assessment approach achieved the highest response rate and accuracy. The proposed approach requires less assessment workload, while achieving the response rate and accuracy similar to the task-assessment approach.

The deployment and provisioning of intelligent systems and utility-based services will greatly benefit from a cloud-based intelligent middleware framework, which could be deployed over multiple infrastructure providers (such as smart cities, hospitals, campus and private enterprises, offices, etc.) in order to deliver on-demand access to smart services. This paper introduces the formulation of an open source integrated intelligent platform as solution for integrating global sensor networks, providing design principles for cloud-based intelligent environments and discuss infrastructure functional modules and their implementation. This paper review briefly technologies enabling the framework, towards emphasizes on demand establishment of smart cities services based on the automated formulation of ubiquitous intelligence of Internet connected objects. The framework introduced founds on the GSN infrastructure. The framework leverages W3C SSN-XG formal language and the IETF COAP protocol, providing support for enabling intelligent (sensors-objects) services. The service requirements for particular smart city scenario are introduced and initial implementations and results from performed simulations studied and discussed.