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The 39th SGAI International Conference on Artificial Intelligence (AI 2019), Cambridge, United Kingdom, 17-19 December 2019 In precision agriculture (PA), soil sampling and testing op-eration is prior to planting any new crop. It is an expensive operationsince there are many soil characteristics to take into account. This papergives an overview of soil characteristics and their relationships with cropyield and soil profiling. We propose an approach for predicting soil pHbased on nearest neighbour fields. It implements spatial radius queriesand various regression techniques in data mining. We use soil dataset containing about 4,000 fields profiles to evaluate them and analyse theirrobustness. A comparative study indicates that LR, SVR, andGBRTtechniques achieved high accuracy, with the R2 values of about 0.718 and MAEvalues of 0.29. The experimental results showed that the pro-posed approach is very promising and can contribute significantly to PA. Science Foundation Ireland Insight Research Centre Origin Enterprises

Digitisation has ushered in a new era of value creation where cross border data flows generate more economic value than traditional flows of goods. The powerful new combination of digital and traditional forms of innovation has seen several new industries branded with a ‘tech’ suffix. In the education technology sector (EdTech), which is the industry context of this research, digitisation is driving double-digit growth into a projected $240 billion industry by 2021. Yet, despite its contemporary significance, the field of entrepreneurship has paid little attention to the phenomenon of digital entrepreneurship. As several scholars observe, digitisation challenges core organising axioms of entrepreneurship, with significant implications for the new venture creation process in new sectors such as EdTech. New venture creation no longer appears to follow discrete and linear models of innovation, as spatial and temporal boundaries get compressed. Given the paradigmatic shift, this study investigates three interrelated themes. Firstly, it seeks to determine how a Pure Digital Entrepreneurship (PDE) process develops over time; and more importantly, how the journey challenges extant assumptions of the entrepreneurial process. Secondly, it strives to identify and theorise the deep structures which underlie the PDE process through mechanism-based explanations. Consequently, the study also seeks to determine the causal pathways and enablers which overtly or covertly interrelate to power new venture emergence and performance. Thirdly, it aims to offer practical guidelines for nurturing the growth of PDE ventures, and for the development of supportive ecosystems. To meet the stated objectives, this study utilises an Insider Action Research (IAR) approach to inquiry, which incorporates reflective practice, collaborative inquiry and design research for third-person knowledge production. This three-pronged approach to inquiry allows for the enactment of a PDE journey in real-time, while acquiring a holistic narrative in the ‘swampy lowlands’ of new venture creation. The findings indicate that the PDE process is differentiated by the centrality of digital artifacts in new venture ideas, which in turn result in less-bounded processes that deliver temporal efficiencies – hence, the shorter new venture creation processes than in traditional forms of entrepreneurship. Further, PDE action is defined by two interrelated events – digital product development and digital growth marketing. These events are characterised by the constant forking, merging and termination of diverse activities. Secondly, concurrent enactment and piecemeal co-creation were found to be consequential mechanisms driving temporal efficiencies in digital product development. Meanwhile, data-driven operation and flexibility combine in digital growth marketing, to form higher order mechanisms which considerably reduce the levels of task-specific and outcome uncertainties. Finally, the study finds that digital growth marketing is differentiated from traditional marketing by the critical role of algorithmic agencies in their capacity as gatekeepers. Thus, unlike traditional marketing, which emphasises customer sovereignty, digital growth marketing involves a dual focus on the needs of human and algorithmic stakeholders. Based on the findings, this research develops a pragmatic model of pure digital new venture creation and suggests critical policy guidelines for nurturing the growth of PDE ventures and ecosystems.

Air quality monitoring in Ireland is under the jurisdiction of the Environmental Protection Agency in compliance with the Gothenburg Protocol, EU/national legislation, and the National Clean Air Strategy. Particulate Matter (PM) has been acknowledged as a key atmospheric pollutant, with serious public health impacts and no safe threshold of exposure in place to-date. Ammonia (NH3) emissions are linked to the secondary production of PM through atmospheric reactions occurring with acidic atmospheric components such as sulfuric acid, nitric acid, and hydrochloric acid. These reactions result in the formation of ammonium sulfate, ammonium nitrate and ammonium chloride, among others. More than 95% of NH3 emissions occurring in Ireland arise from agriculture, with minor contributions from transport and natural sources. This study aims to advance knowledge and understanding of the role of arable agricultural practices and management in NH3 enrichment and aid in mapping of the sources of PM production. The nature and contribution of NH3 in the atmosphere to secondary PM in defined arable settings will be examined to provide greater insight into system dynamics facilitating emission control and mitigation measures to be implemented. This will be achieved through a review of existing literature and database assessment combined with the application of a localised field monitoring network in arable agricultural settings. As Ireland currently has no active atmospheric NH3 monitoring in place, reported emission levels can prove to be imprecise. And lead to over- and under-estimation of NH3 gas emissions to the atmosphere from sources such as agriculture. By establishing localized monitoring stations at emission sources, the precision of the estimated NH3 concentrations in the atmosphere can be improved. This can also lead to improved understanding of PM dynamics and formation. This will be achieved by using a combination of active and passive sampling instruments for in-field atmospheric sample collection, which will then be analysed in the laboratory using ion chromatography. Additionally, to gain a fuller understanding of the dynamics of an agricultural system, background monitoring of soil properties and water nutrient enrichment will also be carried out. The output of this project will build on existing theories of NH3, and PM dynamics established by previous research, and combine these with field data, including agricultural practices, NH3 source production and PM generation, soil and water enrichment and quality background monitoring to synthesise a new mechanistic paradigm. This new understanding will be operationalised through the development of a conceptual model of NH3 dynamics and PM generation, and agri-ecological interactions known as Conceptual Ammonia-aeroSol bIOspheric Simulation (CASIOS). The model builds on a Drivers, Pressures, State, Impacts, Responses framework, with an additional attribute introduced under the term ‘Concept’ which includes environmental conditions previously not considered under this paradigm.

The 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 7-11 June 2020 The mobile service platform envisaged by emerging IoT and 5G is guaranteeing gigabit-level bandwidth, ultra-low latency and ultra-high storage capacity for their subscribers. In spite of the variety of applications plausible with the envisaged technologies, security is a demanding objective that should be applied beyond the design stages. Thus, Security as a Service (SECaaS) is an initiative for a service model that enable mobile and IoT consumers with diverse security functions such as Intrusion Detection and Prevention (IDPaaS), Authentication (AaaS), and Secure Transmission Channel (STCaaS) as a Service. A well-equipped edge computing infrastructure is intrinsic to achieve this goal. The emerging Multi-Access Edge Computing (MEC) paradigm standardized by the ETSI is excelling among other edge computing flavours due to its well-defined structure and protocols. Thus, in our directive, we intend to utilize MEC as the edge computing platform to launch the SECaaS functions. Though, the actual development of a MEC infrastructure is highly dependent on the integration of virtualization technologies to enable dynamic creation, the deployment, and the detachment of virtualized entities that should feature interoperability to cater the heterogeneous IoT devices and services. To that extent, this work is proposing a security service architecture that offers these SECaaS services. Further, we validate our proposed architecture through the development of a virtualized infrastructure that integrates lightweight and hypervisor-based virtualization technologies. Our experiments prove the plausibility of launching multiple security instances on the developed prototype edge platform. European Commission Horizon 2020 University College Dublin

The construction industry contributes significantly to the socio-economic development of nations through infrastructure development, and job creation culminating into the growth of Gross Domestic Product (GDP). Quantity Surveying Professional Service Firms (QSPSFs) play a critical role in the construction industry by ensuring that projects are delivered within cost, required quality and duration by providing technical and knowledge-intensive services to clients, contractors and stakeholders. Irish QSPSFs are facing challenges such as tender price inflation, intense competition, a skills shortage and disruptive technology. These challenges coupled with the cyclicality of the sector create a turbulent business environment for Irish QSPSFs, yet there remains a paucity of empirical evidence pertaining to how strategic decisions are made by these firms. Strategic planning is critical to addressing the challenges confronting business organisations such as the Irish QSPSFs; however, to date strategic planning has focused to a greater extent on manufacturing, oil and gas, retail, consumer products and light manufacturing, whereas there remains limited empirical investigation within the construction industry. This study aims to address this gap by examining the strategic decision-making process of Irish QSPSFs operating in the changing environment of the construction industry. What sets the research apart is that a Dynamic Capabilities (DC) perspective has been used with focus on sensing; seizing; and transformation, culminating into its integration into the development of a strategic decision-making framework. This study is entrenched in the pragmatist philosophical stance with emphasis on the positivist and interpretivist position and adopts mixed method by using quantitative and qualitative approaches over two phases. The first phase involves a survey administered with support from the Society of Chartered Surveyors Ireland (SCSI) to 350 member practices whereby a single senior Quantity Surveyors (QS) in each practice was invited to participate. Seventy-two usable survey questionnaires completed by respondents were prepared for data analysis. The second phase of the research comprised of interview with ten chief executives or managing directors of Irish QSPSFs. The study found the most preferred strategic choice at the corporate level of QSPSFs as the expansion of services to new markets and sectors. At the business level, the investigation discovered the differentiation of services as the main strategic choice of QSPSFs. Furthermore, participation in strategic decision-making is very critical to the success of strategy formulation in organisations. This study identifies the factors that drive participation in strategic decision-making as the knowledge and competence of staff; personality traits; and the ability of people to make decision at the operational level of the organisation. The investigation also found that strategic change has occurred in QSPSFs over the past ten years. This strategic change is attributable to turbulent environmental conditions such as economic recession, in particular reference to the prolong economic recession 2008-2013. The investigation identified the specific strategic changes that occurred in QSPSFs as growth and expansion into new markets; agglomeration, and changes in the ownership and management structure. The negative and positive impacts of economic recession on QSPSFs have also been identified in this investigation. For instance, a radical shift in strategic response from being proactive to reactive; and self-preservation of ownership structure are the ii adverse effects of economic recession identified by the study while knowledge acquisition; and risk profiling for identification and capturing of opportunities are the positive impacts of economic recession. The study found significant statistical evidence to confirm a strong relationship between the turbulent business environment and the strategic decision-making process characteristics of QSPSFs. A strategic decision-making framework was developed on the basis of field work undertaken which was subsequently validated by respondent practices. The framework is the first of its kind pertaining to construction PSFs.

The 2020 IEEE International Conference on Communications Workshops (ICC Workshops), Dublin, Ireland, 7-11 June 2020 This paper proposes “An Emergency Situation Detection System for Ambient Assisted Living (AAL)”, to support elderly people and patients with chronic conditions and potential health-related emergencies to live independently. It implements an Internet of Things (IoT) network that continuously monitors the health conditions of these people. The network includes mobile phones, to transmit the data generated by the IoT sensors to the cloud server. Especially, the paper proposes the 3 rd party unknown mobile relays instead of dedicated gateways as opposed to many existing solutions for IoT healthcare applications. The wireless communication technology used to provide the connectivity between the sensor nodes and mobile relays is Bluetooth Low Energy (BLE). To establish a secure end-to-end connectivity between low power IoT sensor nodes and cloud servers, the paper proposes several techniques. After the medical data transmission to the cloud server, it is responsible for emergency detection and alert generation accordingly. The type of emergency is not limited to a specific health issue, but new emergency situations can be defined and added to the proposed system. Ultimately, the interested parties such as family members, caretakers and doctors receive these alerts. The development of a prototype of the system as a part of the work using commercial off-the-shelf devices verifies the validity of the proposing system and evaluates the performance advantage over the existing systems. European Commission University College Dublin Academy of Finland

As geographical observational data capture, storage and sharing technologies such as in situ remote monitoring systems and spatial data infrastructures evolve, the vision of a Digital Earth, first articulated by Al Gore in 1998 is getting ever closer. However, there are still many challenges and open research questions. For example, data quality, provenance and heterogeneity remain an issue due to the complexity of geo-spatial data and information representation. Observational data are often inadequately semantically enriched by geo-observational information systems or spatial data infrastructures and so they often do not fully capture the true meaning of the associated datasets. Furthermore, data models underpinning these information systems are typically too rigid in their data representation to allow for the ever-changing and evolving nature of geo-spatial domain concepts. This impoverished approach to observational data representation reduces the ability of multi-disciplinary practitioners to share information in an interoperable and computable way. The health domain experiences similar challenges with representing complex and evolving domain information concepts. Within any complex domain (such as Earth system science or health) two categories or levels of domain concepts exist. Those concepts that remain stable over a long period of time, and those concepts that are prone to change, as the domain knowledge evolves, and new discoveries are made. Health informaticians have developed a sophisticated two-level modelling systems design approach for electronic health documentation over many years, and with the use of archetypes, have shown how data, information, and knowledge interoperability among heterogenous systems can be achieved. This research investigates whether two-level modelling can be translated from the health domain to the geo-spatial domain and applied to observing scenarios to achieve semantic interoperability within and between spatial data infrastructures, beyond what is possible with current state-of-the-art approaches. A detailed review of state-of-the-art SDIs, geo-spatial standards and the two-level modelling methodology was performed. A cross-domain translation methodology was developed, and a proof-of-concept geo-spatial two-level modelling framework was defined and implemented. The Open Geospatial Consortium’s (OGC) Observations & Measurements (O&M) standard was re-profiled to aid investigation of the two-level information modelling approach. An evaluation of the method was undertaken using II specific use-case scenarios. Information modelling was performed using the two-level modelling method to show how existing historical ocean observing datasets can be expressed semantically and harmonized using two-level modelling. Also, the flexibility of the approach was investigated by applying the method to an air quality monitoring scenario using a technologically constrained monitoring sensor system. This work has demonstrated that two-level modelling can be translated to the geospatial domain and then further developed to be used within a constrained technological sensor system; using traditional wireless sensor networks, semantic web technologies and Internet of Things based technologies. Domain specific evaluation results show that twolevel modelling presents a viable approach to achieve semantic interoperability between constrained geo-observational sensor systems and spatial data infrastructures for ocean observing and city based air quality observing scenarios. This has been demonstrated through the re-purposing of selected, existing geospatial data models and standards. However, it was found that re-using existing standards requires careful ontological analysis per domain concept and so caution is recommended in assuming the wider applicability of the approach. While the benefits of adopting a two-level information modelling approach to geospatial information modelling are potentially great, it was found that translation to a new domain is complex. The complexity of the approach was found to be a barrier to adoption, especially in commercial based projects where standards implementation is low on implementation road maps and the perceived benefits of standards adherence are low. Arising from this work, a novel set of base software components, methods and fundamental geo-archetypes have been developed. However, during this work it was not possible to form the required rich community of supporters to fully validate geoarchetypes. Therefore, the findings of this work are not exhaustive, and the archetype models produced are only indicative. The findings of this work can be used as the basis to encourage further investigation and uptake of two-level modelling within the Earth system science and geo-spatial domain. Ultimately, the outcomes of this work are to recommend further development and evaluation of the approach, building on the positive results thus far, and the base software artefacts developed to support the approach.

LUWQ 2019: International Interdisciplinary Conference on Land Use and Water Quality. Agriculture and the Environment. Aarhus, Denmark, 3-6 June 2019 Policymakers, farm advisors and water agencies require up-to-date national maps of critical source areas (CSAs) of nitrogen (N) and phosphorus (P) losses from agricultural land to improve catchment management decisions. The DiffuseTools project aimed to achieve this in Ireland by updating the existing Catchment Characterisation Tool and sub-model NCYCLE_IRL, which predicts environmental losses of N and P from the farm via surface runoff, leaching, denitrification and volatilisation. Updates included (i) using improved national maps of farm-scale source loadings as inputs, (ii) sub-field scale modelling of surface transport risk using soil topographic indices derived from 1 m and 5 m NEXTMap digital elevation models (DEMs), (iii) modelling hydrological disconnectivity from microtopography (HSA Index) and reinfiltration (SCIMAP), (iv) improving the national ditch and stream channel network used by the model by DEM extraction, and (v) using SCIMAP to improve predictions of erosion risk. The improved national source loading maps included mean nationally weighted farm-gate N and P imports (fertilizer, feed and livestock) and balance surpluses (kg/ha) calculated for each stocking rate and soil group (land use potential) category within each sector type (dairy, mixed livestock, suckler cattle, non-suckler cattle, sheep and tillage), using annual Teagasc National Farm Survey data (2008-15). Furthermore, updated national maps of soil P and atmospheric N and P deposition inputs were also used within the national source loading maps to improve model performance. National CSA maps for N and P for each pathway were then produced and evaluated using water quality monitoring data and field observations from the Environmental Protection Agency and Teagasc Agricultural Catchments Programme. These maps will be able to support sustainable intensification by informing farm and catchment management decisions such as where to cost effectively target mitigation measures to reduce environmental losses, where to distribute nutrient surpluses (to non-CSAs in nutrient deficit), and improving functional land management. Environmental Protection Agency Check for published version during checkdate report - AC

peer-reviewed Irish Journal of Agricultural and Food Research | Volume 58: Issue 1 The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery R. O’Haraemail , S. Green and T. McCarthy DOI: https://doi.org/10.2478/ijafr-2019-0006 | Published online: 11 Oct 2019 PDF       Abstract Article PDF References Recommendations Abstract The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the flood’s depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include high–temporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales.

The 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), Limerick, Ireland, 15-18 April 2019 This paper provides a research plan focusing on the application of self-learning techniques for energy systems integration in the residential building sector. Demand response is becoming increasingly important in the evolution of the power grid since demand no longer necessarily determines system supply but is now more closely constrained by generation profiles. Demand response can offer energy flexibility services across wholesale and balancing markets. Different applications have focused on the Internet of Things in demand response to assist customers, aggregators and utility companies to manage the energy consumption and energy usage through the adjustment of consumer behaviour. Even though there is extensive work in the literature regarding the potential of the commercial and the residential building sectors to provide flexibility, to date there is no standardised framework to evaluate this flexibility in a customer-Tailored way. At the same time, demand response events may affect occupant comfort expectations hindering the utilisation of flexibility that building energy systems can provide. In this research, the integration of machine learning algorithms into building control systems is investigated, in order to unify the monitoring and control of the separate systems under a holistic approach. This will allow the operation of the systems to be optimised with respect to reducing their energy consumption and their environmental footprint in tandem with the maximisation of flexibility, while maintaining occupant comfort. Science Foundation Ireland