A Big Data Architecture for Log Data Storage and Analysis

We propose an architecture for analysing database connection logs across different instances of databases within an intranet comprising over 10,000 users and associated devices. Our system uses Flume agents to send notifications to a Hadoop Distributed File System for long-term storage and ElasticSearch and Kibana for short-term visualisation, effectively creating a data lake for the extraction of log data. We adopt machine learning models with an ensemble of approaches to filter and process the indicators within the data and aim to predict anomalies or outliers using feature vectors built from this log data

T2Candida assay: diagnostic performance and impact on antifungal prescribing

Objectives: To assess the performance of T2Candida for the diagnosis of invasive candidiasis (IC) against gold standards of candidaemia or consensus IC definitions, and to evaluate the impact of T2Candida on antifungal drug prescribing. Methods: A retrospective review was undertaken of all T2Candida (T2MR technology, T2 Biosystems) performed from October 2020 to February 2022. T2Candida performance was evaluated against confirmed candidaemia or against proven/probable IC within 48 hours of T2Candida, and its impact on antifungal drug prescriptions. Results: T2Candida was performed in 61 patients, with 6 (9.8%) positive results. Diagnostic performance of T2Candida against candidaemia had a specificity of 85.7% and negative predictive value (NPV) of 96.8%. When comparing T2Candida results with consensus definitions of IC, the specificity and NPV of T2Candida was respectively 90% (54/60) and 98.2% (54/55) for proven IC, and 91.4% (53/58) and 96.4% (53/55) for proven/probable IC. Antifungals were initiated in three of six patients (50%) with a positive T2Candida result. Thirty-three patients were receiving empirical antifungals at the time of T2Candida testing, and a negative result prompted cessation of antifungals in 11 (33%) patients, compared with 6 (25%) antifungal prescriptions stopped following negative beta-D-glucan (BDG) testing in a control population (n = 24). Conclusions: T2Candida shows high specificity and NPV compared with evidence of Candida bloodstream infection or consensus definitions for invasive Candida infection, and may play an adjunctive role as a stewardship tool to limit unnecessary antifungal prescriptions

Report on findings on transportation and logistics of selected food value chains:Salmon to fillet case study

• Transportation has significant impact on food costs and the environment. It is a major contributor to carbon emissions, accounting for almost a quarter of the CO2 emissions in the EU, of which 30% is attributed to the food sector. • This deliverable addresses the modelling of food chains’ transportation and logistics. It develops a robust model for policy support, which is applied to a specific case as a worked example. The approach can be used to model the transport and logistics of other food supply chains, given data availability. • The mathematical modelling aims to optimise the cost and effectiveness of logistics operations. It also allows for the integration and consideration of environmental aspects within transportation, processing and distribution operations. • Specifically, the deliverable focuses on the development of a logistics mathematical model using Atlantic salmon as an exemplary example of a globally integrated food supply chain. A Norwegian salmon exporter was engaged to supply data for validating the mathematical model. • The model follows a multi-objective optimization approach that captures the trade-off between total logistics cost and the environment. It has two objectives. Firstly, to minimize total costs associated with transportation, fuel consumption, inventory holding, processing and residuals/waste. Secondly, to reduce CO2 emissions incurred by production at plants, transportation from suppliers to plants, and transportation from plants to customers. • Constraints related to supply, processing capacity, storage capacity, demand, carbon emissions, inventory balancing, transportation capacity, and different modes of transportation between different types of plants and facilities are also consider within the model. • Model development, validation and policy recommendation occurred in four stages: (i) mapping supply chain linkages and product flows, (ii) designing the mathematical model, (iii) data collection for parameters of the model and (iv) model validation and deriving policy recommendation. • Before modeling, consultation with salmon supply chain actors occurred as a first step to map the supply chain linkages. This involved expert interviews with VALUMICS partners. • Based on the mapping of the supply chain, a mathematical model was developed. However, given the complexity of the supply chain and the limited information that can be drawn from a single company which completely covers both the supply and the demand ends of the value chains, the model was divided into two stages (Model N1 and N2) • First it optimises the supply chain network from salmon farms, abattoirs, primary processing plants, secondary processing plants and wholesalers so to meet the demand of the Secondary Processing Plants and Wholesalers for Fresh HOG (Head-on-Gutted) product (Model N1) (farm to wholesaler). • Second, it addresses the supply chain from the secondary processing plants and wholesalers to retailers. The secondary processing plants process HOG into whole fillet, salmon by-products and some residual amount so to meet the demand of retailers (Model N2) (wholesaler to retailer). • An additional model (Model M) allows for the optimisation of the overall supply chain network where, for example, a Company X tries to meet the demand of retailers in different time periods (farm to retailer). • A transportation scenario analysis was also conducted by considering options for various maritime transportation routes from primary processing plant to secondary processing and primary processing plant to various wholesalers. • The results from the three models highlight that it is essential for any company to optimise the overall supply chain network system (from salmon farms to retailers), as the total cost for model M is relatively much lower than the combined total cost of N1 and N2. • Each model also shows that the supply chain network is sensitive to fuel cost and consequently, fuel consumption and distances between actors across the supply chain. • Environmental impact is generally measured by fuel consumption during operations and in the case of food chain, transportation and distribution are important contributors via the use of fuel-based vehicles, sea vessels and/or airplanes. • The scenarios analysis highlights the importance of adopting maritime transportation routes in terms of significantly reducing the total cost, fuel cost and overall carbon emission. Hence shifting certain logistics operations from road to maritime transportation from the perspective of economic and environmental benefits are advocated. • For short to medium distances (vans, trucks, rails and sea vessels) that covers transportation trips to reach airport hubs and big cities, lowering CO2 emissions depends on the emissions ratio (the relative emissions impact of delivery vehicle when compared to personal vehicle – mostly applied in urban logistics) and customer density. • For long distance transport (air), environmental improvement can be mainly achieved through technological development and this has been well supported by research dedicated specifically to address EU aviation industry challenges. • The models are developed for a planning horizon consisting of discrete time periods, aiding the possibility of studying demand and supply uncertainty and its consequences in supply chain decision making. Hence, they help decision makers to identify the changes in a supply chain network when different transportation routes are adopted (for example whether maritime routes can be adopted or not in place of road/rail transportation, to address environmental concerns related to fuel consumption and carbon emissions). • The models are valuable for policy makers in terms of understanding the costs and emissions associated with different food supply chains, as well as the effects of particular policy interventions and market developments (e.g. variation in demand, fuel costs, emission and waste constraints). • They can aid supply chain managers to make decisions regarding the amountof inventory to be kept in different time periods.Aditjandra, P., De, A., M., Gorton, M., Hubbard, C., Pang, G., Mehta, S., Thakur, M., Richardson, M., Bogasson, S., Olafsdottir, G. (2019) Report on findings on transportation and logistics of selected food value chains. VALUMICS "Understanding Food Value Chains and Network Dynamics", funded by European Union's Horizon 2020 research and innovation programme GA No 727243. Deliverable: D7.1, Newcastle University, UK, 94 page

Assessing the Impact of Stakeholder Engagement in Management Strategy Evaluation

After completing a large, regional, multi-use Management Strategy Evaluation, we attempt to assess the impact of stakeholder engagement on the project. We do so by comparing the original project plan to the actual project development and highlight the changes which can be more directly related to stakeholder engagement aided by the application of a logic model for program evaluation. The impact can be summarised into four broad classes: a) change in the actual project development; b) a measurable change in the network of interactions both stakeholders (which includes researchers); c) changes in how the computer model was developed and run; and d) changes in attitudes among stakeholders (including researchers). We discuss these changes, the way they have been detected and some lessons we learnt which may benefit future Management Strategy Evaluation projects

A multi-model approach to stakeholder engagement in complex environmental problems

We describe the different types of models we used as part of an effort to inform policy-making aiming at the management of the Ningaloo coast in the Gascoyne region, Western Australia. This provides an overview of how these models interact, the different roles they cover, how they fit into a full decision making process and what we learnt about the stakeholders involved in our project via their use. When modelling is explicitly used to address socio-ecological issues, the key determinant of success is whether the models, their results and recommendations are taken up by stakeholders; such uptake in turn depends on addressing stakeholders’ concerns, on engaging them in the project, on ensuring they feel ownership of the decision process at large, and that they understand and trust the modelling effort. This observation has guided our approach and has resulted in treating ‘building a model’ as the catalyst, rather than the final aim, of the process. In other words, extensive interactions in order to introduce, showcase, discuss and tune the model used for final decision making have represented both a requirement and an opportunity to ensure (i) model relevance, (ii) its acceptance, (iii) that all information available in the stakeholder team was accounted for and (iv) that stakeholders holding different levels of understanding of modelling, what it does and what it can provide to decision-making could develop an informed opinion on its use. To fulfil these roles we developed five broad classes of models: conceptual models, toy-models, singlesystem models, shuttle-models and a full-system model. In conceptual models the main drivers of a system are highlighted for subsequent representation as components of the full-system model. This usually results in a diagram summarising our understanding of how the system works. In toy-models a problem is simplified in such a way that only a handful of components are included. The purpose of these models is mostly educational: we want to understand how each component affects the problem and in order to achieve this, we temporarily renounce a satisfactory understanding of the overall problem. In single-system models we include a fairly detailed representation of a single component of the system (in our case recreational fishing and tourism); these models can be used to introduce stakeholders to modelling, provide temporary results from the study of a single activity, which will feed into the development of the final full-system model, or address sector-specific issues. In shuttle-models, we include the minimum number of processes we believe are crucial for a basic understanding of the overall problem. We know these models are still too simple for full system description, but they provide a sufficient understanding to enable us to contemplate, build and use the more complex models needed for full problem description. The term ‘shuttle’ refers to taking us from a minimum to a full description of the problem, a journey which is necessary both to developers in model definition and parameterisation and to stakeholders in the interpretation of the final full-system model results. Finally, the full-system model includes all information collected through the project and addresses all scenarios of stakeholders concern, and whose definition has been greatly eased by use of the ‘simpler’ models. As an example, a conceptual model may identify fishing and tourism as the main drivers of a region; a toymodel may describe how catches affect fish stocks; a single-system model may include the effect of gear, regulations and other processes affecting recreational fishing; a shuttle-model may include a simplified representation of the interaction between fishing, tourism, and infrastructure development on the overall health of the local ecosystem; this will gradually ‘take’ us to comprehend the ‘full’ model which may include tourism pressure, fish market values, climate effect, larger food-webs, etc