Improving market intelligence for organic horticulture in Wales

The organic market in the UK reached a value of £1.015 billion during the 2002/2003 financial year. The market for organic food and drink in Wales and the surrounding counties is estimated to account for 6.3 per cent of this, a total of £64 million. Assuming the proportion of sales of organic fruit and vegetables in Wales is the same as the UK as a whole then the market for organic fruit and vegetables in Wales can be valued at £20.5 million for the financial year 2002/03. The area of fully organic horticultural land in Wales in April 2003 was 513 hectares with 102 producers involved in organic fruit and vegetable production. Of these producers, over 60 per cent were mixed farms encompassing livestock and/or cereals alongside horticulture. The estimated farm-gate value of Welsh produced organic fruit and vegetables was £1.8 million, approaching 5 per cent of the total UK farm-gate value of £43.96 million. Organic horticultural production accounts for a significant proportion - 10 per cent - of the total horticultural land in Wales. In comparison, organic horticulture in the UK as a whole accounts for just 4 per cent of the total horticultural land. Interviews with Welsh growers identified a number of key challenges facing them including market access, lack of producer co-operation, increased competition (particularly from within the UK) and availability of labour. At the beginning of 2003, 103 licensed organic processing operations were operating in Wales with approximately one-third handling fruit and vegetables. The main constraints to increasing the utilisation of domestic produce were identified as continuity, quality, accessibility and reliability of supply. Key recommendations to help overcome many of the challenges identified are included in Chapter 6 - Conclusions and Recommendations. Fruit and vegetables are a key entry point for consumers beginning to buy organic food. Welsh consumers are less ‘put-off’ by the price of organic food than consumers across the UK as a whole. Supporting the local farmers is particularly important to the Welsh consumer, with 35 per cent of the Welsh organic buying public stating that it was important to support local farmers, compared to 16 per cent in the UK overall

Web based lecture technologies: blurring the boundaries between face to face and distance learning

Web based lecture technologies (WBLT) have gained popularity amongst universities in Australia as a tool for delivering lecture recordings to students in close to real time. This paper reports on a selection of results from a larger research project investigating the impact of WBLT on teaching and learning. Results show that while staff see the advantages for external students, they question the extent to which these advantages apply to internal students. In contrast both cohorts of students were positive about the benefits of the technologies for their learning and they adopted similar strategies for their use. With the help of other technologies, some external students and staff even found WBLT useful for fostering communication between internal and external students. As such, while the traditional boundary between internal and external students seems to remain for some staff, students seem to find the boundary much less clear

Free-Enterprise Farming on Grasslands in Central NSW, Australia

A pathway is described in developing and improving the pastures on family-owned sheep and beef properties at sites near Blayney in central NSW. Initially, the twin approach of sowing perennial grasses, predominantly phalaris (Phalaris aquatica) with subterranean clover (Trifolium subterraneum) plus the recommended addition of superphosphate fertiliser, was closely followed but within a decade ill-thrift in pastures and livestock occurred. Once the core problem of soil acidity was recognised, steps were taken to overcome this constraint with applications of lime. However, an additional modification involving the application of gypsum with lime had to be sorted out and applied. This approach is explained. While recent drought conditions on the Central Tablelands/Slopes have been a factor in reducing the productivity of district pastures, an important part of the problem is a consequence of many landowners not understanding the basic principles of plant and livestock nutrition, an unwillingness of some research/advisory agronomists to recognise the expertise of successful producers, and the implementation of various farmer subsidy and support schemes that appear to reward dependent producers rather than encouraging independence

ALOJA: A framework for benchmarking and predictive analytics in Hadoop deployments

This article presents the ALOJA project and its analytics tools, which leverages machine learning to interpret Big Data benchmark performance data and tuning. ALOJA is part of a long-term collaboration between BSC and Microsoft to automate the characterization of cost-effectiveness on Big Data deployments, currently focusing on Hadoop. Hadoop presents a complex run-time environment, where costs and performance depend on a large number of configuration choices. The ALOJA project has created an open, vendor-neutral repository, featuring over 40,000 Hadoop job executions and their performance details. The repository is accompanied by a test-bed and tools to deploy and evaluate the cost-effectiveness of different hardware configurations, parameters and Cloud services. Despite early success within ALOJA, a comprehensive study requires automation of modeling procedures to allow an analysis of large and resource-constrained search spaces. The predictive analytics extension, ALOJA-ML, provides an automated system allowing knowledge discovery by modeling environments from observed executions. The resulting models can forecast execution behaviors, predicting execution times for new configurations and hardware choices. That also enables model-based anomaly detection or efficient benchmark guidance by prioritizing executions. In addition, the community can benefit from ALOJA data-sets and framework to improve the design and deployment of Big Data applications.This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 639595). This work is partially supported by the Ministry of Economy of Spain under contracts TIN2012-34557 and 2014SGR1051.Peer ReviewedPostprint (published version

SimaticScan:towards a specialised vulnerability scanner for industrial control systems

Over the years, modern Industrial Control Systems (ICS) have become widely computerised and connected via the Internet and are, therefore, potentially vulnerable to cyber attacks. Currently there is a lack of vulnerability scanners specialised to ICS settings. Systems such as PLCScan and ModScan output pertinent information from a Programmable Logic Controller (PLC). However, they do not offer any information as to how vulnerable a PLC is to an attack. In this paper, we address these limitations and propose SimaticScan, a vulnerability scanner specialised to Siemens SIMATIC PLCs. Through experimentation in a comprehensive water treatment testbed, we demonstrate SimaticScan’s effectiveness in determining a range of vulnerabilities across three distinct PLCs, including a previously unknown vulnerability in one of the PLCs. Our experiments also show that SimaticScan outperforms the widely used Nessus vulnerability scanner (with relevant ICS-specific plugins deployed)

Comparative Analysis of European Examples of Freight Electric Vehicles Schemes—A Systematic Case Study Approach with Examples from Denmark, Germany, the Netherlands, Sweden and the UK.

E-Mobility is a hot topic, in the public policy area as well as in business and scientific communities. Literature on electric freight transport is still relatively scarce. Urban freight transport is considered as one of the most promising fields of application of vehicle electrification, and there are on-going demonstration projects. This paper will discuss case study examples of electric freight vehicle initiatives in Denmark, Germany, the Netherlands, Sweden and the UK and identify enablers and barriers for common trends

Extreme Dysbiosis of the Microbiome in Critical Illness.

Critical illness is hypothesized to associate with loss of "health-promoting" commensal microbes and overgrowth of pathogenic bacteria (dysbiosis). This dysbiosis is believed to increase susceptibility to nosocomial infections, sepsis, and organ failure. A trial with prospective monitoring of the intensive care unit (ICU) patient microbiome using culture-independent techniques to confirm and characterize this dysbiosis is thus urgently needed. Characterizing ICU patient microbiome changes may provide first steps toward the development of diagnostic and therapeutic interventions using microbiome signatures. To characterize the ICU patient microbiome, we collected fecal, oral, and skin samples from 115 mixed ICU patients across four centers in the United States and Canada. Samples were collected at two time points: within 48 h of ICU admission, and at ICU discharge or on ICU day 10. Sample collection and processing were performed according to Earth Microbiome Project protocols. We applied SourceTracker to assess the source composition of ICU patient samples by using Qiita, including samples from the American Gut Project (AGP), mammalian corpse decomposition samples, childhood (Global Gut study), and house surfaces. Our results demonstrate that critical illness leads to significant and rapid dysbiosis. Many taxons significantly depleted from ICU patients versus AGP healthy controls are key "health-promoting" organisms, and overgrowth of known pathogens was frequent. Source compositions of ICU patient samples are largely uncharacteristic of the expected community type. Between time points and within a patient, the source composition changed dramatically. Our initial results show great promise for microbiome signatures as diagnostic markers and guides to therapeutic interventions in the ICU to repopulate the normal, "health-promoting" microbiome and thereby improve patient outcomes. IMPORTANCE Critical illness may be associated with the loss of normal, "health promoting" bacteria, allowing overgrowth of disease-promoting pathogenic bacteria (dysbiosis), which, in turn, makes patients susceptible to hospital-acquired infections, sepsis, and organ failure. This has significant world health implications, because sepsis is becoming a leading cause of death worldwide, and hospital-acquired infections contribute to significant illness and increased costs. Thus, a trial that monitors the ICU patient microbiome to confirm and characterize this hypothesis is urgently needed. Our study analyzed the microbiomes of 115 critically ill subjects and demonstrated rapid dysbiosis from unexpected environmental sources after ICU admission. These data may provide the first steps toward defining targeted therapies that correct potentially "illness-promoting" dysbiosis with probiotics or with targeted, multimicrobe synthetic "stool pills" that restore a healthy microbiome in the ICU setting to improve patient outcomes

Curating the Palaeolithic

This guidance explains the importance of the English Palaeolithic record (about 1 million to 11,700 years ago) in its Pleistocene context and best practices for protecting it through the planning process, illustrated by case studies from across the country. The guidance focuses on the curation of the Palaeolithic and Pleistocene records (Part A) and is particularly intended for curators. It also provides background information on the Palaeolithic and Pleistocene records for those less familiar with the period (Part B). The guidance document is accompanied by a number of concise online case studies that illustrate a range of approaches for curating the Palaeolithic record. The document is intended to be used alongside other key guidance produced by Historic England, and acknowledges the primacy of relevant legislation. It is not a statement of Government policy, nor does it seek to prescribe a single methodology

ALOJA-ML: a framework for automating characterization and knowledge discovery in Hadoop deployments

This article presents ALOJA-Machine Learning (ALOJA-ML) an extension to the ALOJA project that uses machine learning techniques to interpret Hadoop benchmark performance data and performance tuning; here we detail the approach, efficacy of the model and initial results. The ALOJA-ML project is the latest phase of a long-term collaboration between BSC and Microsoft, to automate the characterization of cost-effectiveness on Big Data deployments, focusing on Hadoop. Hadoop presents a complex execution environment, where costs and performance depends on a large number of software (SW) configurations and on multiple hardware (HW) deployment choices. Recently the ALOJA project presented an open, vendor-neutral repository, featuring over 16.000 Hadoop executions. These results are accompanied by a test bed and tools to deploy and evaluate the cost-effectiveness of the different hardware configurations, parameter tunings, and Cloud services. Despite early success within ALOJA from expert-guided benchmarking, it became clear that a genuinely comprehensive study requires automation of modeling procedures to allow a systematic analysis of large and resource-constrained search spaces. ALOJA-ML provides such an automated system allowing knowledge discovery by modeling Hadoop executions from observed benchmarks across a broad set of configuration parameters. The resulting empirically-derived performance models can be used to forecast execution behavior of various workloads; they allow a-priori prediction of the execution times for new configurations and HW choices and they offer a route to model-based anomaly detection. In addition, these models can guide the benchmarking exploration efficiently, by automatically prioritizing candidate future benchmark tests. Insights from ALOJA-ML's models can be used to reduce the operational time on clusters, speed-up the data acquisition and knowledge discovery process, and importantly, reduce running costs. In addition to learning from the methodology presented in this work, the community can benefit in general from ALOJA data-sets, framework, and derived insights to improve the design and deployment of Big Data applications.This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 re- search and innovation programme (grant agreement No 639595). This work is partially supported by the Ministry of Economy of Spain under contracts TIN2012-34557 and 2014SGR105Peer ReviewedPostprint (published version