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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.

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.

The 29th International Joint Conference on Artificial Intelligence - 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI-20), Yokohama, Japan, January 2021 (Conference postponed due to COVID-19 pandemic) Smart agriculture (SmartAg) has emerged as a rich domain for AI-driven decision support systems (DSS); however, it is often challenged by user-adoption issues. This paper reports a case-based reasoning system, PBI-CBR, that predicts grass growth for dairy farmers, that combines predictive accuracy and explanations to improve user adoption. PBI-CBR’s key novelty is its use of Bayesian methods for case-base maintenance in a regression domain. Experiments report the tradeoff between predictive accuracy and explanatory capability for different variants of PBI-CBR, and how updating Bayesian priors each year improves performance. Science Foundation Ireland Insight Research Centre

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 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

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.

Post-harvest life of fresh produce is limited due to high metabolic activity and microbial spoilage. Modified atmosphere packaging (MAP) has proven to be one of the most effective techniques to extend the shelf life of fresh produce commercially. Obtaining of an optimum concentration of oxygen and carbon dioxide inside the package depends upon the product properties, the environmental conditions of the cold chain, the permeable film, some of which are subjected to natural variability during the food distribution chain. This variability may generate produce that is out of specification that will lead to food waste. Uncertainty analysis of this problem may lead to relevant interventions to prevent these losses. The hypothesis of this work was to create a mathematical model that predicts key quality factors for MAP packaged fresh products in the supply chain distribution, which will help to assess the food losses in relation to quality thresholds. The model developed simulated the respiration rate as function of O2 and CO2 concentration and produce temperature using Michaelis-Menten equations. The exchange of gases (O2, CO2) and water vapour between the fruit surface, package atmosphere and external atmosphere was modelled taking into account the process of transpiration and condensation. In the transpiration model, the fresh produce surface was assumed to be perfectly saturated and the energy of respiration was used to evaporate surface water. Temperature changes in the headspace due to metabolic heat, convective heat transfer and heat exchange by gas transmission through the package were accounted for. The quality attributes of fresh produce included weight loss and colour change (L, a, and b values) for mushroom, from Botrytis and its fermentative activity for strawberry and weight loss and spoilage for tomato. ii These conditions were simulated for real and variable i) export cold chain and ii) retail display storage to evaluate the effect of cold chain variability (temperature and relative humidity) on the quality of fresh produce and associated waste generation. The prediction of propagation of biological variance on the quality of fresh produce during storage was obtained using a mathematical model. Sensitivity analysis of the stochastic MAP model pointed out the influence of input parameters on the quality of fresh produce. The conclusions of the study showed that the toolbox developed is able to interpret cold chain data: 1) mathematical prediction of quality; 2) simulation of cold chain conditions allowing for different variability components; 3) estimation of waste generation kinetics based in all quality criteria and thresholds; 4) sensitivity analysis to identify the most sensitive technological parameters; and 5) identification of interventions that affect the benchmarked technological parameters.

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

The Environmental and Sustainable Resource Management (ESRM) Post-graduate Research Day, University College Dublin, Ireland, 6 December 2019 The inherent factor of poor site productivity in western peatland forests combined with the reduction in management intensity from increased environmental considerations has brought some new challenges into forest management. Our study investigates new, alternative forest management models in the area chosen for this study, Cloosh forest, Co. Galway, to assess how these forests should be managed under future impacts of climate change and dynamic timber prices due to an expanding bioeconomy, and to quantify the impact this will have on forest ecosystem services (ES). Department of Agriculture, Food and the Marine European Commission Horizon 2020

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.