Coal tailings as a soil conditioner : evaluation of tailing properties and effect on tomato plants

The global coal industry yields a vast amount of tailings waste, and the utilisation of these tailings necessitates innovative eforts contributing to the United Nations Sustainable Development Goals. One of such novel initiatives is to reuse coal tailings (CT) safely, ecofriendly, and cost-efectively in agroecosystems as a soil conditioner to enhance the productivity of lands. This study aimed to evaluate the potential utilisation of coal tailings waste in the soil amelioration to improve plant performance. The physico–chemical characteristics of coal tailings from two Australian mining sites (CT1 and CT2) showed that the tailings samples are alkaline with loamy and loamy sand textures, respectively. The tailings have ~ 3% of macronutrients, high carbon (C), and low heavy metals and metalloids (As, Cd, Se, Cu, Zn, and Pb). The germination rate of tomato seeds was improved in the low-rate CT treatment. Greenhouse tomato plants exhibited an increase in leaf’s K, Ca, and Mg contents in CT1 and CT2 treatments. More importantly, the CT treatment-induced accumulation of heavy metals in plants was mostly insignifcant in both CT treatments. Therefore, we highlight the potential application of coal tailings as a soil conditioner because of the benefcial efect of improved carbon and nutrients (N, P, K, Mg, and Ca) in tomato leaves. Further amendment of the coal tailings should focus on the adjustment of pH and the addition of other benefcial materials for the improvement of soil properties for crops in both the greenhouse and the feld

Evolving, dynamic clustering of spatio/spectro-temporal data in 3D spiking neural network models and a case study on EEG data

Clustering is a fundamental data processing technique. While clustering of static (vector based) data and of fixed window size time series have been well explored, dynamic clustering of spatiotemporal data has been little researched if at all. Especially when patterns of changes (events) in the data across space and time have to be captured and understood. The paper presents novel methods for clustering of spatiotemporal data using the NeuCube spiking neural network (SNN) architecture. Clusters of spatiotemporal data were created and modified on-line in a continuous, incremental way, where spatiotemporal relationships of changes in variables are incrementally learned in a 3D SNN model and the model connectivity and spiking activity are incrementally clustered. Two clustering methods were proposed for SNN, one performed during unsupervised and one—during supervised learning models. Before submitted to the models, the data is encoded as spike trains, a spike representing a change in the variable value (an event). During the unsupervised learning, the cluster centres were predefined by the spatial locations of the input data variables in a 3D SNN model. Then clusters are evolving during the learning, i.e. they are adapted continuously over time reflecting the dynamics of the changes in the data. In the supervised learning, clusters represent the dynamic sequence of neuron spiking activities in a trained SNN model, specific for a particular class of data or for an individual instance. We illustrate the proposed clustering method on a real case study of spatiotemporal EEG data, recorded from three groups of subjects during a cognitive task. The clusters were referred back to the brain data for a better understanding of the data and the processes that generated it. The cluster analysis allowed to discover and understand differences on temporal sequences and spatial involvement of brain regions in response to a cognitive task

Developmenrt of EST-SSR and genomic-SSR markers to assess genetic diversity in Jatropha Curcas L.

<p>Abstract</p> <p>Background</p> <p><it>Jatropha curcas L. </it>has attracted a great deal of attention worldwide, regarding its potential as a new biodiesel crop. However, the understanding of this crop remains very limited and little genomic research has been done. We used simple sequence repeat (SSR) markers that could be transferred from <it>Manihot esculenta </it>(cassava) to analyze the genetic relationships among 45 accessions of <it>J. curcas </it>from our germplasm collection.</p> <p>Results</p> <p>In total, 187 out of 419 expressed sequence tag (EST)-SSR and 54 out of 182 genomic (G)-SSR markers from cassava were polymorphic among the <it>J. curcas </it>accessions. The EST-SSR markers comprised 26.20% dinucleotide repeats, 57.75% trinucleotide repeats, 7.49% tetranucleotide repeats, and 8.56% pentanucleotide repeats, whereas the majority of the G-SSR markers were dinucleotide repeats (62.96%). The 187 EST-SSRs resided in genes that are involved mainly in biological and metabolic processes. Thirty-six EST-SSRs and 20 G-SSRs were chosen to analyze the genetic diversity among 45 <it>J. curcas </it>accessions. A total of 183 polymorphic alleles were detected. On the basis of the distribution of these polymorphic alleles, the 45 accessions were classified into six groups, in which the genotype showed a correlation with geographic origin. The estimated mean genetic diversity index was 0.5572, which suggests that our <it>J. curcas </it>germplasm collection has a high level of genetic diversity. This should facilitate subsequent studies on genetic mapping and molecular breeding.</p> <p>Conclusion</p> <p>We identified 241 novel EST-SSR and G-SSR markers in <it>J. curcas</it>, which should be useful for genetic mapping and quantitative trait loci analysis of important agronomic traits. By using these markers, we found that the intergroup gene diversity of <it>J. curcas </it>was greater than the intragroup diversity, and that the domestication of the species probably occurred partly in America and partly in Hainan, China.</p

Value-added products as soil conditioners for sustainable agriculture

Due to the intensive use of fertilisers, soil degradation has become a global problem, leading to the depletion of organic matter and soil fertility. Meanwhile, the intensification of agriculture accompanied by urbanisation and industrialisation has drastically accelerated the waste generation rate. For instance, coal mining produces wastes in a large quantity globally, the majority of which end up in landfills or dump into storage dams. Accordingly, sustainable food production is driving global innovations to better utilise various waste materials to make value added products, such as soil conditioners. Nowadays, soil conditioners are of great importance to improve plant growth and soil health and reduce chemical fertiliser use. This paper comprehensively reviews the soil conditioners derived from various agro-wastes and coal by-products. The process of producing soil conditioners and their sustainable applications in agriculture are also reviewed. Furthermore, sustainable approaches to recycle coal wastes are gaining increasing interest, and co-pelletisation of coal waste with agro-waste as a value-added soil conditioner to supplement soil nutrients in the agro-ecosystem has been proposed to improve the productivity of lands towards sustainable agricultural applications. This review highlights the possibility of turning coal wastes and organic wastes into revenue-earning products of environmental and economic values in the form of pellets for soil conditioning. But a multidisciplinary approach should be adopted to utilise the natural resources eco-friendly and cost-effectively, contributing to the United Nations Sustainable Development Goals

Value-Added Products from Coal Tailings

The quantity of coal tailings (CT) currently discharged annually in Qld and NSW is estimated to be between 14 and 16 million dry tonnes with the majority going into tailings storage facilities (TSF). Due to the continuous mining activities over the last 50 years, the existing tailings storage in Australia has reached an order of 500 million tonnes. Currently, over 200 million tonnes of aggregates and 4.9 million tonnes of fertilisers are used in Australia per annum. As the demand for aggregates and soil conditioners is very high, a large proportion of tailings could be potentially consumed for the production of aggregates and soil conditioners to be used locally in mining areas and surrounding communities. This project aimed to develop a new tailings management solution by using tailings to produce road subbase aggregates and agricultural soil conditioners that can generate revenue from coal wastes and reduce the impact of CT on our environment. For this purpose, coal tailings from four mining sites were collected and investigated further