Analysis of a Shaftless Semi-Hard Magnetic Material Flywheel on Radial Hysteresis Self-Bearing Drives

Flywheel Energy Storage Systems are interesting solutions for energy storage, featuring advantageous characteristics when compared to other technologies. This has motivated research effort focusing mainly on cost aspects, system reliability and energy density improvement. In this context, a novel shaftless outer-rotor layout is proposed. It features a semi-hard magnetic FeCrCo 48/5 rotor coupled with two bearingless hysteresis drives. The novelty lies in the use of the semi-hard magnetic material, lending the proposed layout advantageous features thanks to its elevated mechanical strength and magnetic properties that enable the use of bearingless hysteresis drives. The paper presents a study of the proposed layout and an assessment of its energetic features. It also focuses on the modeling of the radial magnetic suspension, where the electromagnets providing the levitating forces are modeled through a one-dimensional approach. The Jiles–Atherton model is used to describe the magnetic hysteresis of the rotor material. The proposed flywheel features a mass of 61.2 kg, a storage capability of 600 Wh at the maximum speed of 18,000 rpm and achieves an energy density of 9.8 Wh/kg. The performance of the magnetic suspension is demonstrated to be satisfactory and the influence of the hysteresis of the rotor material is highlighted

An update on the global strategy for the conservation and utilisation of tropical and subtropical forage genetic resources

Tropical and sub-tropical forages (TSTF) are critically important for supplying livestock feed and environmental benefits in extensive and intensive livestock systems of developed and developing countries. There has been focused collection and conservation of forage genetic resources (FGR), and research on their diversity, adaptation and use for the past 60 years. This laid the foundations for the impacts TSTF have had, and continue to have. However, since about 1995 there has been significant reduction in forage science investment, and capability globally, and that has strangely coincided with the accelerated demand for livestock products. The status of TSTF germplasm conservation, capability and capacity are now at risk, and the decline must be reversed if the tropical and subtropical farming systems are to access the best genetic material and knowledge to meet the growing food/environmental needs. A strategy to reduce barriers to TSTF conservation, research and utilisation was developed under the Global Crop Diversity Trust in 2015 with input from across the TSTF-genetic resources community. Its aim was to build a functional network of national, regional and international genetic resource centres, introduce operational efficiencies, and enable genebanks to improve their role as knowledge managers and advisors for research and development programs. The strategy’s main objectives are: 1) Rebuild the community of TSTF genebanks and genebank users to develop closer collaboration and trust; 2) Ensure more efficient and rationalized conservation within and among genebanks; and 3) Actively support utilisation by anticipating germplasm needs and responding to users’ requests for information and seeds. Implementation of the strategy commenced in 2016, with the first aim being to win buy-in and cooperation of international and national genebanks. A new Newsletter, ‘Forages for the Future’, has >600 recipients and reports key implementation activities and the roles of forages across the tropics and subtropics. Making recent impacts more widely known indirectly helps build the body of evidence that improved forages deliver impacts and is the basis for growth in financial and human resources invested in TSTF. The CGIAR genebanks of ILRI and CIAT play key roles in TSTF research and use. In recognition of the need for greater efficiencies and better utilisation of the germplasm, ILRI and CIAT have undertaken an ambitious program to align collections to provide a one stop portal, with prioritised species/accessions for conservation and research, and a simplified germplasm request process. This change is occurring simultaneously with a TSTF strategy initiative encouraging some key national TSTF centres to work more closely together and with the CGIAR centres and with the update of the widely used TSTF database and selection tool, SoFT, with new content and ability to be used on smart phones. That new version will be released in 2019. Reversing the past downward trend requires the commitment and long-term engagement of partner countries and the donor community. The alternative is that 60 years of knowledge and expertise will have to be rebuilt, and generations of farmers and other users will not realize the production and environmental benefits that well-adapted and sustainably managed improved forages can attain

Application of random-forest machine learning algorithm for mineral predictive mapping of Fe-Mn crusts in the World Ocean

Mineral prospectivity mapping constitutes an efficient tool for delineating areas of highest interest to guide future exploration. Multiple knowledge-driven approaches have been applied for the creation of prospectivity maps for deep-sea ferromanganese (Fe-Mn) crusts over the last decades. The results of a data-driven approach making use of an extensive data collection exercise on occurrences of Fe-Mn crusts in the World Ocean and recent increase in global marine datasets are presented. A Random Forest machine learning algorithm is applied, and results compared with previously established expert-driven maps. Optimal predictive conditions for the algorithm are observed for (i) a forest size superior to a hundred trees, (ii) a training dataset larger than 10%, and (iii) a number of predictors to be used as nodes superior to two. The confusion matrix and out-of-bag errors on the remaining unused data highlight excellent predictive capabilities of the trained model with a prediction accuracy for Fe-Mn crusts of 87.2% and 98.2% for non-crusts locations, with a Kohen’s K index of 0.84, validating its application for prediction at the World scale. The slope of the seafloor, sediment thickness, sediment type, biological productivity, and abyssal mountain constitute the five strongest explanatory variables in predicting the occurrence of Fe-Mn crusts. Most ‘hand-drawn’ knowledge-driven prospective areas are also considered prospective by the random forest algorithm with notable exceptions along the coast of the American continent. However, poor correlation is observed with knowledge-driven GIS-based criterion mapping as the Random Forest considers un-prospective most target areas from the GIS approach. Overall, the Random Forest prediction performs better in predicting a high chance of Fe-Mn crust occurrence in ISA licensed area than the GIS approach, which constitutes an external validation of the predictive quality of the random forest model

Integrated Object-Based Image Analysis for semi-automated geological lineament detection in southwest England

Regional lineament detection for mapping of geological structure can provide crucial information for mineral exploration. Manual methods of lineament detection are time consuming, subjective and unreliable. The use of semi-automated methods reduces the subjectivity through applying a standardised method of searching. Object-Based Image Analysis (OBIA) has become a mainstream technique for landcover classification, however, the use of OBIA methods for lineament detection is still relatively under-utilised. The Southwest England region is covered by high-resolution airborne geophysics and LiDAR data that provide an excellent opportunity to demonstrate the power of OBIA methods for lineament detection. Herein, two complementary but stand-alone OBIA methods for lineament detection are presented which both enable semi-automatic regional lineament mapping. Furthermore, these methods have been developed to integrate multiple datasets to create a composite lineament network. The top-down method uses threshold segmentation and sub-levels to create objects, whereas the bottom-up method segments the whole image before merging objects and refining these through a border assessment. Overall lineament lengths are longest when using the top-down method which also provides detailed metadata on the source dataset of the lineament. The bottom-up method is more objective and computationally efficient and only requires user knowledge to classify lineaments into major and minor groups. Both OBIA methods create a similar network of lineaments indicating that semi-automatic techniques are robust and consistent. The integration of multiple datasets from different types of spatial data to create a comprehensive, composite lineament network is an important development and demonstrates the suitability of OBIA methods for enhancing lineament detection

A machine learning approach to tungsten prospectivity modelling using knowledge-driven feature extraction and model confidence

Novel mineral prospectivity modelling presented here applies knowledge-driven feature extraction to a data-driven machine learning approach for tungsten mineralisation. The method emphasises the importance of appropriate model evaluation and develops a new Confidence Metric to generate spatially refined and robust exploration targets. The data-driven Random Forest™ algorithm is employed to model tungsten mineralisation in SW England using a range of geological, geochemical and geophysical evidence layers which include a depth to granite evidence layer. Two models are presented, one using standardised input variables and a second that implements fuzzy set theory as part of an augmented feature extraction step. The use of fuzzy data transformations mean feature extraction can incorporate some user-knowledge about the mineralisation into the model. The typically subjective approach is guided using the Receiver Operating Characteristics (ROC) curve tool where transformed data are compared to known training samples. The modelling is conducted using 34 known true positive samples with 10 sets of randomly generated true negative samples to test the random effect on the model. The two models have similar accuracy but show different spatial distributions when identifying highly prospective targets. Areal analysis shows that the fuzzy-transformed model is a better discriminator and highlights three areas of high prospectivity that were not previously known. The Confidence Metric, derived from model variance, is employed to further evaluate the models. The new metric is useful for refining exploration targets and highlighting the most robust areas for follow-up investigation. The fuzzy-transformed model is shown to contain larger areas of high model confidence compared to the model using standardised variables. Finally, legacy mining data, from drilling reports and mine descriptions, is used to further validate the fuzzy-transformed model and gauge the depth of potential deposits. Descriptions of mineralisation corroborate that the targets generated in these models could be undercover at depths of less than 300 ​m. In summary, the modelling workflow presented herein provides a novel integration of knowledge-driven feature extraction with data-driven machine learning modelling, while the newly derived Confidence Metric generates reliable mineral exploration targets

A high-resolution 4200 year record of lake-level changes, ecosystem dynamics and anthropogenic activity in the Andean highlands

Environmental and cultural changes in the Central Andean region can most successfully be understood through multidisciplinary approaches, combining archaeological and historical research with detailed reconstruction of past climate and vegetation. Here we present a multi-proxy dataset from a small, infilled lake basin in the Cuzco region of Peru that reflects the ecosystem dynamics of both terrestrial and semi-aquatic/aquatic communities. Marcacocha (altitude 3355 m) has a morphology and location that renders it extremely sensitive to environmental change. Organic sediments cored from the centre of the basin have provided a unique insight into how this lake system has responded to local/regional climatic forcings and anthropogenic pressures over the last 4200 years. A series of quasi-periodic, sustained arid episodes have been identified using sedge pollen abundances as a proxy for lake-level variations. Many of these episodes correspond to periods of significant cultural turnover in the independently-dated archaeological record. In particular, data point to the importance of a warm and arid interval that began ~AD 900, which may correspond to the Northern Hemisphere Medieval Warm Period. This interval, which lasted until the end of the 15th century, saw the decline of the Wari Empire (and the Tiwanaku around Lake Titicaca) at ~AD 1100 and allowed the development of the Inca State, the largest Empire ever seen in the New World (~AD 1400-1532). Verifying how these arid climatic periods may have impacted on human societies is difficult, however, given that pre-Spanish Andean cultures failed to develop any form of written record. We therefore present a new method of reconstructing rural socio-economic shifts from the analysis of the frequency of oribatid mite remains. Oribatid mites are soil-dwelling microarthropod detritivores, some of which inhabit areas of grassland pasture. One of the primary controls governing their abundance in such habitats is the level of animal dung present. We propose that past fluctuations in mite remains can be related to the density of domestic animals using the pasture and, by extension, may provide a proxy for broad-scale social and economic change through time. The 6-yr resolution of the Marcacocha record is ideal for testing this hypothesis, by comparing the timing and magnitude of mite fluctuations since the 1530s with a series of well-documented socio-economic shifts in the region that relate to past political and climatic pressures. Results demonstrate remarkable correspondence between the two datasets, providing the confidence to extend the record back a further 3700 years

Evaluation of Rapid, Early Warning Approaches to Track Shellfish Toxins Associated with Dinophysis and Alexandrium Blooms

Marine biotoxin-contaminated seafood has caused thousands of poisonings worldwide this century. Given these threats, there is an increasing need for improved technologies that can be easily integrated into coastal monitoring programs. This study evaluates approaches for monitoring toxins associated with recurrent toxin-producing Alexandrium and Dinophysis blooms on Long Island, NY, USA, which cause paralytic and diarrhetic shellfish poisoning (PSP and DSP), respectively. Within contrasting locations, the dynamics of pelagic Alexandrium and Dinophysis cell densities, toxins in plankton, and toxins in deployed blue mussels (Mytilus edulis) were compared with passive solid-phase adsorption toxin tracking (SPATT) samplers filled with two types of resin, HP20 and XAD-2. Multiple species of wild shellfish were also collected during Dinophysis blooms and used to compare toxin content using two different extraction techniques (single dispersive and double exhaustive) and two different toxin analysis assays (liquid chromatography/mass spectrometry and the protein phosphatase inhibition assay (PP2A)) for the measurement of DSP toxins. DSP toxins measured in the HP20 resin were significantly correlated (R2 = 0.7–0.9, p < 0.001) with total DSP toxins in shellfish, but were detected more than three weeks prior to detection in deployed mussels. Both resins adsorbed measurable levels of PSP toxins, but neither quantitatively tracked Alexandrium cell densities, toxicity in plankton or toxins in shellfish. DSP extraction and toxin analysis methods did not differ significantly (p > 0.05), were highly correlated (R2 = 0.98–0.99; p < 0.001) and provided complete recovery of DSP toxins from standard reference materials. Blue mussels (Mytilus edulis) and ribbed mussels (Geukensia demissa) were found to accumulate DSP toxins above federal and international standards (160 ng g−1) during Dinophysis blooms while Eastern oysters (Crassostrea virginica) and soft shell clams (Mya arenaria) did not. This study demonstrated that SPATT samplers using HP20 resin coupled with PP2A technology could be used to provide early warning of DSP, but not PSP, events for shellfish management