Global distribution of modern shallow marine shorelines. Implications for exploration and reservoir analogue studies

Acknowledgments Support for this work came from the SAFARI consortium which was funded by Bayern Gas, ConocoPhillips, Dana Petroleum, Dong Energy, Eni Norge, GDF Suez, Idemitsu, Lundin, Noreco, OMV, Repsol, Rocksource, RWE, Statoil, Suncor, Total, PDO, VNG and the Norwegian Petroleum Directorate (NPD). This manuscript has benefited from discussion with Bruce Ainsworth, Rachel Nanson and Christian Haug Eide. Boyan Vakarelov and Richard Davis Jr. are thanked for their constructive reviews and valuable comments that helped to improve the manuscript.Peer reviewedPostprin

Organic beef and sheep production in the uplands

The organic unit at ADAS Redesdale was established to evaluate the physical and financial implications of converting a progressive hill/upland unit to an organic system. Conversion of 400 ha, 600 breeding ewes (in 3 flocks) and 35 suckler cows was completed in 1993. One organic flock (Organic Dipper ) was managed as a direct comparison with a conventionally managed system (Conventional Dipper). During the early years following conversion, an organic system was developed which, financially, enabled the organic unit to compete favourably with a comparable conventional system. This was on the basis of maintaining similar stocking rates, and pushing the organic system towards maximum output. As the experiment progressed, it became increasingly clear that a different balance of farming and environmental objectives were required if the broader ecological and ethical objectives of organic farming were to be better met. Stocking rate reductions had been made in two of the organically managed flocks (Cairn and Burnhead) in 1995. On the basis of the divergence in flock and individual animal performance, and following recommendations by the Project Steering Committee, sheep stocking rates were reduced by 25% on the Organic Dipper flock from November 2000. From mating in November 2001, breeding ewe numbers on the Burnhead flock were reduced by a further 45% in line with Countryside Stewardship prescriptions. The overall objective of the study was to compare the long-term performance of organic and conventional hill and upland systems. The project was funded as a one-year extension, pending a review of DEFRA’s organic research programme. The 2000/01 production year covered in this report, represented the eighth year under full organic production, and coincided with the redirection of management on the unit towards better integration with agri-environmental objectives. Data were collected on physical and financial performance, animal health and welfare, and market performance. However, the outbreak of foot and mouth disease in February 2001, forced management changes, which significantly affected the systems comparison. Absolute results therefore need to be viewed against this background. Eight commercial organic farms were costed to provide information on physical and financial performance (related to 2000 born lamb and calf crops) in support of the main study. Stocking rates varied from 0.6 to 1.6 grazing livestock units (GLU) per adjusted hectare. Across the sample of farms, the effect of replacing HLCA (Hill Farming Compensatory Allowance) with HFA (Hill Farming Allowance) in 2000 was generally neutral, but tended to favour more extensive systems. Performance of sheep and cattle were within expected limits for hill and upland production. Farm output (£ per adjusted hectare) was £770, £407, and £592 for linked farms, Newcastle University and IRS FBS costed farms respectively. Whole farm gross margin averaged £587/adj. ha (range £265 – £628), representing extensive and value added production systems respectively. Fixed costs ranged from £332 - £498, compared with fixed costs of £184 and £337 for Newcastle and IRS respectively. Based on the identical sample of five farms, average Net Farm Income (NFI) was £46/adj. ha higher in 2000 than in the previous year. On all but one farm, NFI was equal or lower than the value of manual labour from the farmer and his spouse, resulting in a negative Management and Investment Income. It is difficult to draw precise conclusions on the performance of the organic unit during 2001, given the disruption to management caused by FMD. However, the relative physical performance of organic and conventional systems was broadly in line with previous years. Choice of stocking level and the availability of market premia for organic stock will have a profound effect on animal performance and economic return. Good levels of technical performance are increasingly important, as the price differentials between organic and conventional beef and lamb are eroded. Added value strategies such as direct selling, can significantly boost returns, but are not universally applicable. To generate significant ecological improvements a much more proactive management approach is required. Information from the linked farm study shows that organic farmers are generally willing to spend money on conservation projects. However, in order to make this investment a level of underlying profitability is required. The linked farm study also shows that once conversion aid payments are no longer payable, only a minority of organic hill/upland beef and sheep farms make a significant profit. With profitability increasingly fragile, and organic beef imports running at approximately 35%, any major changes in the organic standards which increase the costs of production could have a disproportionate effect

Enhancing Big Data Feature Selection Using a Hybrid Correlation-Based Feature Selection

This study proposes an alternate data extraction method that combines three well-known feature selection methods for handling large and problematic datasets: the correlation-based feature selection (CFS), best first search (BFS), and dominance-based rough set approach (DRSA) methods. This study aims to enhance the classifier’s performance in decision analysis by eliminating uncorrelated and inconsistent data values. The proposed method, named CFS-DRSA, comprises several phases executed in sequence, with the main phases incorporating two crucial feature extraction tasks. Data reduction is first, which implements a CFS method with a BFS algorithm. Secondly, a data selection process applies a DRSA to generate the optimized dataset. Therefore, this study aims to solve the computational time complexity and increase the classification accuracy. Several datasets with various characteristics and volumes were used in the experimental process to evaluate the proposed method’s credibility. The method’s performance was validated using standard evaluation measures and benchmarked with other established methods such as deep learning (DL). Overall, the proposed work proved that it could assist the classifier in returning a significant result, with an accuracy rate of 82.1% for the neural network (NN) classifier, compared to the support vector machine (SVM), which returned 66.5% and 49.96% for DL. The one-way analysis of variance (ANOVA) statistical result indicates that the proposed method is an alternative extraction tool for those with difficulties acquiring expensive big data analysis tools and those who are new to the data analysis field.Ministry of Higher Education under the Fundamental Research Grant Scheme (FRGS/1/2018/ICT04/UTM/01/1)Universiti Teknologi Malaysia (UTM) under Research University Grant Vot-20H04, Malaysia Research University Network (MRUN) Vot 4L876SPEV project, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic (ID: 2102–2021), “Smart Solutions in Ubiquitous Computing Environments

Knowledge reduction of dynamic covering decision information systems with varying attribute values

Knowledge reduction of dynamic covering information systems involves with the time in practical situations. In this paper, we provide incremental approaches to computing the type-1 and type-2 characteristic matrices of dynamic coverings because of varying attribute values. Then we present incremental algorithms of constructing the second and sixth approximations of sets by using characteristic matrices. We employ experimental results to illustrate that the incremental approaches are effective to calculate approximations of sets in dynamic covering information systems. Finally, we perform knowledge reduction of dynamic covering information systems with the incremental approaches

Attribute Equilibrium Dominance Reduction Accelerator (DCCAEDR) Based on Distributed Coevolutionary Cloud and Its Application in Medical Records

© 2013 IEEE. Aimed at the tremendous challenge of attribute reduction for big data mining and knowledge discovery, we propose a new attribute equilibrium dominance reduction accelerator (DCCAEDR) based on the distributed coevolutionary cloud model. First, the framework of N-populations distributed coevolutionary MapReduce model is designed to divide the entire population into N subpopulations, sharing the reward of different subpopulations' solutions under a MapReduce cloud mechanism. Because the adaptive balancing between exploration and exploitation can be achieved in a better way, the reduction performance is guaranteed to be the same as those using the whole independent data set. Second, a novel Nash equilibrium dominance strategy of elitists under the N bounded rationality regions is adopted to assist the subpopulations necessary to attain the stable status of Nash equilibrium dominance. This further enhances the accelerator's robustness against complex noise on big data. Third, the approximation parallelism mechanism based on MapReduce is constructed to implement rule reduction by accelerating the computation of attribute equivalence classes. Consequently, the entire attribute reduction set with the equilibrium dominance solution can be achieved. Extensive simulation results have been used to illustrate the effectiveness and robustness of the proposed DCCAEDR accelerator for attribute reduction on big data. Furthermore, the DCCAEDR is applied to solve attribute reduction for traditional Chinese medical records and to segment cortical surfaces of the neonatal brain 3-D-MRI records, and the DCCAEDR shows the superior competitive results, when compared with the representative algorithms