Probability functions to build composite indicators: a methodology to measure environmental impacts of genetically modified crops

There is an on-going debate on the environmental effects of genetically modified crops to which this paper aims to contribute. First, data on environmental impacts of genetically modified (GM) and conventional crops are collected from peer-reviewed journals, and secondly an analysis is conducted in order to examine which crop type is less harmful for the environment. Published data on environmental impacts are measured using an array of indicators, and their analysis requires their normalisation and aggregation. Taking advantage of composite indicators literature, this paper builds composite indicators to measure the impact of GM and conventional crops in three dimensions: (1) non-target key species richness, (2) pesticide use, and (3) aggregated environmental impact. The comparison between the three composite indicators for both crop types allows us to establish not only a ranking to elucidate which crop is more convenient for the environment but the probability that one crop type outperforms the other from an environmental perspective. Results show that GM crops tend to cause lower environmental impacts than conventional crops for the analysed indicators.

An evidence-based review on the likely economic and environmental impact of genetically modified cereals and oilseeds for UK agriculture

An evidence-based review of the potential impact that the introduction of genetically-modified (GM) cereal and oilseed crops could have for the UK was carried out. The inter-disciplinary research project addressed the key research questions using scenarios for the uptake, or not, of GM technologies. This was followed by an extensive literature review, stakeholder consultation and financial modelling. The world area of canola, oilseed rape (OSR) low in both erucic acid in the oil and glucosinolates in the meal, was 34M ha in 2012 of which 27% was GM; Canada is the lead producer but it is also grown in the USA, Australia and Chile. Farm level effects of adopting GM OSR include: lower production costs; higher yields and profits; and ease of farm management. Growing GM OSR instead of conventional OSR reduces both herbicide usage and environmental impact. Some 170M ha of maize was grown in the world in 2011 of which 28% was GM; the main producers are the USA, China and Brazil. Spain is the main EU producer of GM maize although it is also grown widely in Portugal. Insect resistant (IR) and herbicide tolerant (HT) are the GM maize traits currently available commercially. Farm level benefits of adopting GM maize are lower costs of production through reduced use of pesticides and higher profits. GM maize adoption results in less pesticide usage than on conventional counterpart crops leading to less residues in food and animal feed and allowing increasing diversity of bees and other pollinators. In the EU, well-tried coexistence measures for growing GM crops in the proximity of conventional crops have avoided gene flow issues. Scientific evidence so far seems to indicate that there has been no environmental damage from growing GM crops. They may possibly even be beneficial to the environment as they result in less pesticides and herbicides being applied and improved carbon sequestration from less tillage. A review of work on GM cereals relevant for the UK found input trait work on: herbicide and pathogen tolerance; abiotic stress such as from drought or salinity; and yield traits under different field conditions. For output traits, work has mainly focussed on modifying the nutritional components of cereals and in connection with various enzymes, diagnostics and vaccines. Scrutiny of applications submitted for field trial testing of GM cereals found around 9000 applications in the USA, 15 in Australia and 10 in the EU since 1996. There have also been many patent applications and granted patents for GM cereals in the USA for both input and output traits;an indication of the scale of such work is the fact that in a 6 week period in the spring of 2013, 12 patents were granted relating to GM cereals. A dynamic financial model has enabled us to better understand and examine the likely performance of Bt maize and HT OSR for the south of the UK, if cultivation is permitted in the future. It was found that for continuous growing of Bt maize and HT OSR, unless there was pest pressure for the former and weed pressure for the latter, the seed premia and likely coexistence costs for a buffer zone between other crops would reduce the financial returns for the GM crops compared with their conventional counterparts. When modelling HT OSR in a four crop rotation, it was found that gross margins increased significantly at the higher levels of such pest or weed pressure, particularly for farm businesses with larger fields where coexistence costs would be scaled down. The impact of the supply of UK-produced GM crops on the wider supply chain was examined through an extensive literature review and widespread stakeholder consultation with the feed supply chain. The animal feed sector would benefit from cheaper supplies of raw materials if GM crops were grown and, in the future, they might also benefit from crops with enhanced nutritional profile (such as having higher protein levels) becoming available. This would also be beneficial to livestock producers enabling lower production costs and higher margins. Whilst coexistence measures would result in increased costs, it is unlikely that these would cause substantial changes in the feed chain structure. Retailers were not concerned about a future increase in the amount of animal feed coming from GM crops. To conclude, we (the project team) feel that the adoption of currently available and appropriate GM crops in the UK in the years ahead would benefit farmers, consumers and the feed chain without causing environmental damage. Furthermore, unless British farmers are allowed to grow GM crops in the future, the competitiveness of farming in the UK is likely to decline relative to that globally.

A critical account of the relationship between institutional trust, risk perception, and technology acceptance with an application to genetically modified foods

This article critically reflects on the widely held view of a causal chain with trust in public authorities impacting technology acceptance via perceived risk. It first puts forward conceptual reason against this view, as the presence of risk is a precondition for trust playing a role in decision making. Second, results from consumer surveys in Italy and Germany are presented that support the associationist model as counter hypothesis. In that view, trust and risk judgments are driven by and thus simply indicators of higher order attitudes toward a certain technology which determine acceptance instead. The implications of these findings are discussed.

Trust in authorities monitoring the distribution of genetically modified foods: dimensionality, measurement issues, and determinants

Based on a combined internet and mail survey in Germany the independence of indica-tors of trust in public authorities from indicators of attitudes toward genetically modified food is tested. Despite evidence of a link between trust indicators on the one hand and evaluation of benefits and perceived likelihoods of risks, correlation with other factors is found to be moderate on average. But the trust indicators exhibit only a moderate relation with the re-spondents’ preference for either sole public control or a cooperation of public and private bodies in the monitoring of GM food distribution. Instead, age and location in either the New or the Old Lander are found to be significantly related with such preferences.

Bit-index sort: a fast non-comparison integer sorting algorithm for permutations

This paper describes a fast integer sorting algorithm, herein referred as Bit-index sort, which is a non-comparison sorting algorithm for partial per-mutations, with linear complexity order in execution time. Bit-index sort uses a bit-array to classify input sequences of distinct integers, and exploits built-in bit functions in C compilers supported by machine hardware to retrieve the ordered output sequence. Results show that Bit-index sort outperforms in execution time to quicksort and counting sort algorithms. A parallel approach for Bit-index sort using two simultaneous threads is included, which obtains speedups up to 1.6.

Soil conditioning and plant-soil feedbacks in a modified forest ecosystem are soil-context dependent

Aims There is potential for altered plant-soil feedback (PSF) to develop in human-modified ecosystems but empirical data to test this idea are limited. Here, we compared the PSF operating in jarrah forest soil restored after bauxite mining in Western Australia with that operating in unmined soil. Methods Native seedlings of jarrah (Eucalyptus marginata), acacia (Acacia pulchella), and bossiaea (Bossiaea ornata) were grown in unmined and restored soils to measure conditioning of chemical and biological properties as compared with unplanted control soils. Subsequently, acacia and bossiaea were grown in soils conditioned by their own or by jarrah seedlings to determine the net PSF. Results In unmined soil, the three plant species conditioned the chemical properties but had little effect on the biological properties. In comparison, jarrah and bossiaea conditioned different properties of restored soil while acacia did not condition this soil. In unmined soil, neutral PSF was observed, whereas in restored soil, negative PSF was associated with acacia and bossiaea. Conclusions Soil conditioning was influenced by soil context and plant species. The net PSF was influenced by soil context, not by plant species and it was different in restored and unmined soils. The results have practical implications for ecosystem restoration after human activities.

Spatial structuring of arbuscular mycorrhizal communities in benchmark and modified temperate eucalypt woodlands

Arbuscular mycorrhizal fungi (AMF) are crucial to the functioning of the plant–soil system, but little is known about the spatial structuring of AMF communities across landscapes modified by agriculture. AMF community composition was characterized across four sites in the highly cleared south-western Australian wheatbelt that were originally dominated by forb-rich eucalypt woodlands. Environmentally induced spatial structuring in AMF composition was examined at four scales: the regional scale associated with location, the site scale associated with past management (benchmark woodlands with no agricultural management history, livestock grazing, recent revegetation), the patch scale associated with trees and canopy gaps, and the fine scale associated with the herbaceous plant species beneath which soils were sourced. Field-collected soils were cultured in trap pots; then, AMF composition was determined by identifying spores and through ITS1 sequencing. Structuring was strongest at site scales, where composition was strongly related to prior management and associated changes in soil phosphorus. The two fields were dominated by the genera Funneliformis and Paraglomus, with little convergence back to woodland composition after revegetation. The two benchmark woodlands were characterized by Ambispora gerdemannii and taxa from Gigasporaceae. Their AMF communities were strongly structured at patch scales associated with trees and gaps, in turn most strongly related to soil N. By contrast, there were few patterns at fine scales related to different herbaceous plant species, or at regional scales associated with the 175 km distance between benchmark woodlands. Important areas for future investigation are to identify the circumstances in which recolonization by woodland AMF may be limited by fungal propagule availability, reduced plant diversity and/or altered chemistry in agricultural soils.

Search engine liability for autocomplete suggestions: personality, privacy and the power of the algorithm

This article is concerned with the liability of search engines for algorithmically produced search suggestions, such as through Google’s ‘autocomplete’ function. Liability in this context may arise when automatically generated associations have an offensive or defamatory meaning, or may even induce infringement of intellectual property rights. The increasing number of cases that have been brought before courts all over the world puts forward questions on the conflict of fundamental freedoms of speech and access to information on the one hand, and personality rights of individuals— under a broader right of informational self-determination—on the other. In the light of the recent judgment of the Court of Justice of the European Union (EU) in Google Spain v AEPD, this article concludes that many requests for removal of suggestions including private individuals’ information will be successful on the basis of EU data protection law, even absent prejudice to the person concerned.

Multi-attribute collaborative filtering recommendation based on improved group decision-making

Currently researchers in the field of personalized recommendations bear little consideration on users' interest differences in resource attributes although resource attribute is usually one of the most important factors in determining user preferences. To solve this problem, the paper builds an evaluation model of user interest based on resource multi-attributes, proposes a modified Pearson-Compatibility multi-attribute group decision-making algorithm, and introduces an algorithm to solve the recommendation problem of k-neighbor similar users. Considering the characteristics of collaborative filtering recommendation, the paper addresses the issues on the preference differences of similar users, incomplete values, and advanced converge of the algorithm. Thus the paper realizes multi-attribute collaborative filtering. Finally, the effectiveness of the algorithm is proved by an experiment of collaborative recommendation among multi-users based on virtual environment. The experimental results show that the algorithm has a high accuracy on predicting target users' attribute preferences and has a strong anti-interference ability on deviation and incomplete values.

Differences between the CME fronts tracked by an expert, an automated algorithm, and the Solar Stormwatch project

Observations from the Heliospheric Imager (HI) instruments aboard the twin STEREO spacecraft have enabled the compilation of several catalogues of coronal mass ejections (CMEs), each characterizing the propagation of CMEs through the inner heliosphere. Three such catalogues are the Rutherford Appleton Laboratory (RAL)-HI event list, the Solar Stormwatch CME catalogue, and, presented here, the J-tracker catalogue. Each catalogue uses a different method to characterize the location of CME fronts in the HI images: manual identification by an expert, the statistical reduction of the manual identifications of many citizen scientists, and an automated algorithm. We provide a quantitative comparison of the differences between these catalogues and techniques, using 51 CMEs common to each catalogue. The time-elongation profiles of these CME fronts are compared, as are the estimates of the CME kinematics derived from application of three widely used single-spacecraft-fitting techniques. The J-tracker and RAL-HI profiles are most similar, while the Solar Stormwatch profiles display a small systematic offset. Evidence is presented that these differences arise because the RAL-HI and J-tracker profiles follow the sunward edge of CME density enhancements, while Solar Stormwatch profiles track closer to the antisunward (leading) edge. We demonstrate that the method used to produce the time-elongation profile typically introduces more variability into the kinematic estimates than differences between the various single-spacecraft-fitting techniques. This has implications for the repeatability and robustness of these types of analyses, arguably especially so in the context of space weather forecasting, where it could make the results strongly dependent on the methods used by the forecaster.

A robust sclera segmentation algorithm

Sclera segmentation is shown to be of significant importance for eye and iris biometrics. However, sclera segmentation has not been extensively researched as a separate topic, but mainly summarized as a component of a broader task. This paper proposes a novel sclera segmentation algorithm for colour images which operates at pixel-level. Exploring various colour spaces, the proposed approach is robust to image noise and different gaze directions. The algorithm’s robustness is enhanced by a two-stage classifier. At the first stage, a set of simple classifiers is employed, while at the second stage, a neural network classifier operates on the probabilities’ space generated by the classifiers at stage 1. The proposed method was ranked the 1st in Sclera Segmentation Benchmarking Competition 2015, part of BTAS 2015, with a precision of 95.05% corresponding to a recall of 94.56%.

A Genetic Algorithm for the selection of structural MRI features for classification of Mild Cognitive Impairment and Alzheimer's Disease

This work investigates the problem of feature selection in neuroimaging features from structural MRI brain images for the classification of subjects as healthy controls, suffering from Mild Cognitive Impairment or Alzheimer’s Disease. A Genetic Algorithm wrapper method for feature selection is adopted in conjunction with a Support Vector Machine classifier. In very large feature sets, feature selection is found to be redundant as the accuracy is often worsened when compared to an Support Vector Machine with no feature selection. However, when just the hippocampal subfields are used, feature selection shows a significant improvement of the classification accuracy. Three-class Support Vector Machines and two-class Support Vector Machines combined with weighted voting are also compared with the former and found more useful. The highest accuracy achieved at classifying the test data was 65.5% using a genetic algorithm for feature selection with a three-class Support Vector Machine classifier.

Efficiency of evolutionary algorithms in water network pipe sizing

The pipe sizing of water networks via evolutionary algorithms is of great interest because it allows the selection of alternative economical solutions that meet a set of design requirements. However, available evolutionary methods are numerous, and methodologies to compare the performance of these methods beyond obtaining a minimal solution for a given problem are currently lacking. A methodology to compare algorithms based on an efficiency rate (E) is presented here and applied to the pipe-sizing problem of four medium-sized benchmark networks (Hanoi, New York Tunnel, GoYang and R-9 Joao Pessoa). E numerically determines the performance of a given algorithm while also considering the quality of the obtained solution and the required computational effort. From the wide range of available evolutionary algorithms, four algorithms were selected to implement the methodology: a PseudoGenetic Algorithm (PGA), Particle Swarm Optimization (PSO), a Harmony Search and a modified Shuffled Frog Leaping Algorithm (SFLA). After more than 500,000 simulations, a statistical analysis was performed based on the specific parameters each algorithm requires to operate, and finally, E was analyzed for each network and algorithm. The efficiency measure indicated that PGA is the most efficient algorithm for problems of greater complexity and that HS is the most efficient algorithm for less complex problems. However, the main contribution of this work is that the proposed efficiency ratio provides a neutral strategy to compare optimization algorithms and may be useful in the future to select the most appropriate algorithm for different types of optimization problems.

Modelling personal thermal sensations using C-Support Vector Classification (C-SVC) algorithm

The personalised conditioning system (PCS) is widely studied. Potentially, it is able to reduce energy consumption while securing occupants’ thermal comfort requirements. It has been suggested that automatic optimised operation schemes for PCS should be introduced to avoid energy wastage and discomfort caused by inappropriate operation. In certain automatic operation schemes, personalised thermal sensation models are applied as key components to help in setting targets for PCS operation. In this research, a novel personal thermal sensation modelling method based on the C-Support Vector Classification (C-SVC) algorithm has been developed for PCS control. The personal thermal sensation modelling has been regarded as a classification problem. During the modelling process, the method ‘learns’ an occupant’s thermal preferences from his/her feedback, environmental parameters and personal physiological and behavioural factors. The modelling method has been verified by comparing the actual thermal sensation vote (TSV) with the modelled one based on 20 individual cases. Furthermore, the accuracy of each individual thermal sensation model has been compared with the outcomes of the PMV model. The results indicate that the modelling method presented in this paper is an effective tool to model personal thermal sensations and could be integrated within the PCS for optimised system operation and control.

Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks

With the fast development of wireless communications, ZigBee and semiconductor devices, home automation networks have recently become very popular. Since typical consumer products deployed in home automation networks are often powered by tiny and limited batteries, one of the most challenging research issues is concerning energy reduction and the balancing of energy consumption across the network in order to prolong the home network lifetime for consumer devices. The introduction of clustering and sink mobility techniques into home automation networks have been shown to be an efficient way to improve the network performance and have received significant research attention. Taking inspiration from nature, this paper proposes an Ant Colony Optimization (ACO) based clustering algorithm specifically with mobile sink support for home automation networks. In this work, the network is divided into several clusters and cluster heads are selected within each cluster. Then, a mobile sink communicates with each cluster head to collect data directly through short range communications. The ACO algorithm has been utilized in this work in order to find the optimal mobility trajectory for the mobile sink. Extensive simulation results from this research show that the proposed algorithm significantly improves home network performance when using mobile sinks in terms of energy consumption and network lifetime as compared to other routing algorithms currently deployed for home automation networks.