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International Trends in Mining Tailings Research through Machine Learning Method: Retrospective or Prospective Oriented Research?
Abstract and Figures
Using a conceptual distinction between prospective and retrospective research, we analyze the international high-impact literature on tailings. This distinction differentiates between investigations concentrated on specific case studies, where the socio-environmental consequences of tailings are addressed or specific actions on them are documented, and the investigations that model interventions on tailings, propose new forms of design, management and remediation applicable to tailings. Using Natural Language Processing (NLP) tools, we covered all the publications registered in the Scopus database between 2010 and 2020 on mining tailings. Our research question ask for the temporal orientations in the international publication on mining tailings deposits between 2010 and 2020? Our results show an increase in the number of prospective investigations, which practically double the retrospective ones. However, at the level of citations, this difference is reduced and the most cited investigations in the retrospective perspective outnumber the most cited ones in the prospective perspective. We conclude by discussing the need to address the impact of prospective research on mining companies and overcoming resistance to innovation in the industry when there are no regulatory or legal obligations. In the same way, we call for an increase in the public contribution to maintain the independence of retrospective research, without neglecting the necessary construction of updated evidence on the socio-environmental consequences of mining tailings.
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... Research on design, management, remediation and recovery of water or minerals from tailings processes is included in this group. This classification is in line with the one used by Campos-Medina et al.(2023), which holds that retrospective research, in its aim to address the socio-environmental impacts of tailings, analyzes the past, while prospective research seeks to transform tailings management and operation by projecting into the future (Campos-Medina et al., 2023) . 2 In this way we can assess and compare the findings and practical applications of our research and also discuss general insights from the publications on tailings in Chile in relation to the international publications on the matter. It is important to highlight that the papers in the international academic database presented by Campos-Medina et al. (2023) includes 8434 articles exclusively from Scopus records, whereas our present database, in its attempt to cover the majority of academic papers that address mine tailings in Chile, works with papers published in both Scopus and Web of Sciences. ...
... This classification is in line with the one used by Campos-Medina et al.(2023), which holds that retrospective research, in its aim to address the socio-environmental impacts of tailings, analyzes the past, while prospective research seeks to transform tailings management and operation by projecting into the future (Campos-Medina et al., 2023) . 2 In this way we can assess and compare the findings and practical applications of our research and also discuss general insights from the publications on tailings in Chile in relation to the international publications on the matter. It is important to highlight that the papers in the international academic database presented by Campos-Medina et al. (2023) includes 8434 articles exclusively from Scopus records, whereas our present database, in its attempt to cover the majority of academic papers that address mine tailings in Chile, works with papers published in both Scopus and Web of Sciences. Therefore, the comparison between the findings from the international and Chilean databases described in the results section (4.1) is made only for illustrative purposes and is not intended to be a thorough analysis. ...
As a result of recent socio-environmental disasters, mine tailings have become an environmental liability, raising concern among local communities, civil society and the State. This paper describes the present state of the art of research into tailings deposits in Chile through the analysis of academic publications. The study uses a subject classification model to reveal the dynamics and transformation of research over the last decade. The results show two main areas of research: one concerning the impact of tailings on the country and communities; and the other to the interventions that seek to reduce further impacts of tailings. The discussion section offers an interpretative analysis, which shows that in addition to the attention that is already being constantly paid to the impact of tailings, there has also been a considerable increase in the number of technical studies focused on providing new tailings management solutions during the period from 2015 to 2020. In both research approaches there have been case studies showing the adverse effect of regional agglomeration dynamics and national tailings similarities aimed at a better analysis, monitoring and governance. Recommendations are made to incentivize basic research, as well to broaden applied research.
Radioactive levels and radiological hazard assessment associated with exposure to radionuclides from uranium tailings were investigated in this study. Herein, the concentrations of ²³⁸U, ²²⁶Ra, ²³²Th, and ⁴⁰K in 36 samples including 25 soil and 11 water samples were measured using a low background HPGe semiconductor detector. The radionuclide concentrations were much lower outside the tailings reservoir than inside the reservoir; however, both exceeded the national average and world average values. The mean concentrations of ²³⁸U, ²²⁶Ra, ²³²Th, and ⁴⁰K in water samples were higher than the WHO guideline levels. The radionuclide transfer factors showed that the radionuclides in soil of the tailings enter the surrounding water. Additionally, the radiological hazard indices around the reservoir were much higher than the worldwide average. Results suggest that the high level of radiation in the tailings reservoir posed a potential threat to the health of the local residents.
In this study, the potential of copper tailing deposits in Chile for the sequestration of carbon dioxide (CO2) via ex-situ mineral carbonation integrating the recovery of valuable metals was assessed. An inventory of tailing deposits and CO2 sources existing in Chile was constructed to determine the most suitable site for the installation of a future mineral carbonation plant and to evaluate the technical, economic, and environmental feasibility of CO2 capture, separation, and transport from the source to the mineral carbonation plant. The data of the inventory of tailings deposits in Chile were obtained from the National Service of Geology and Mining. For the thermoelectric plants installed in Chile, data of energy production were obtained from the Energy National Commission. Through the use of the technique for order preference by similarity to ideal solution (TOPSIS) method and sensitivity analysis, the optimum location in the region of Antofagasta to install a mineral carbonation plant was identified. In addition, the results show that in the region of Antofagasta five tailing deposits have the potential to sequester between 66 to 99 Mt of CO2. Meanwhile, thermoelectric plants in 2018 produced about 9.4 Mt of CO2 that is available to be sequestered, with a maximum generation potential of 21.9 Mt of CO2eq per year. The methodology and the study presented can be considered as a preliminary study to identify tailings that require further analysis.
There are high technological and software demands associated with conducting Brain–Computer Interface (BCI) research. In order to accelerate the development and accessibility of BCIs, it is worthwhile to focus on open-source and community desired tooling. Python, a prominent computer language, has emerged as a language of choice for many research and engineering purposes. In this article, BciPy, an open-source, Python-based software for conducting BCI research is presented. It was developed with a focus on restoring communication using Event-Related Potential (ERP) spelling interfaces; however, it may be used for other non-spelling and non-ERP BCI paradigms. Major modules in this system include support for data acquisition, data queries, stimuli presentation, signal processing, signal viewing and modeling, language modeling, task building, and a simple Graphical User Interface (GUI).
To help researchers conduct a systematic review or meta-analysis as efficiently and transparently as possible, we designed a tool to accelerate the step of screening titles and abstracts. For many tasks—including but not limited to systematic reviews and meta-analyses—the scientific literature needs to be checked systematically. Scholars and practitioners currently screen thousands of studies by hand to determine which studies to include in their review or meta-analysis. This is error prone and inefficient because of extremely imbalanced data: only a fraction of the screened studies is relevant. The future of systematic reviewing will be an interaction with machine learning algorithms to deal with the enormous increase of available text. We therefore developed an open source machine learning-aided pipeline applying active learning: ASReview. We demonstrate by means of simulation studies that active learning can yield far more efficient reviewing than manual reviewing while providing high quality. Furthermore, we describe the options of the free and open source research software and present the results from user experience tests. We invite the community to contribute to open source projects such as our own that provide measurable and reproducible improvements over current practice. It is a challenging task for any research field to screen the literature and determine what needs to be included in a systematic review in a transparent way. A new open source machine learning framework called ASReview, which employs active learning and offers a range of machine learning models, can check the literature efficiently and systemically.
Past research has examined parental support for math during early childhood using parent-report surveys and observational measures of math talk. However, since most studies only present findings from one of these methods, the construct (parental support for early math) and the method are inextricably linked, and we know little about whether these methods provide similar or unique information about children’s exposure to math concepts. This study directly addresses the mono-operation bias operating in past research by collecting and comparing multiple measures of support for number and spatial skills, including math talk during semi-structured observations of parent–child interactions, parent reports on a home math activities questionnaire, and time diaries. Findings from 128 parents of 4-year-old children reveal substantial within-measure variability across all three data sources in the frequency of number and spatial activities and the type and content of parent talk about number and spatial concepts. Convergence in parental math support measures was evident among parent reports from the questionnaire and time diaries, such that scale composites about monthly number activities were related to number activities on the previous work day, and monthly spatial activities were correlated with spatial activities the prior non-work days. However, few parent report measures from the survey or time diary were significantly correlated with observed quantity or type of math talk in the semi-structured observations. Future research implications of these findings are discussed.
Mining is very vital to the production of goods, services and infrastructure; it advances the quality of lives in the
society. However, the possible hazard of waste and radioactivity generated by mining, dumping and tailing, has
called on the society to find ways of seeking remedy that will adequately treat mining waste from mine dump,
tailing and abandoned mine. Mine waste reuse and recycling in mining industries could offer cost-effective
benefits through offsetting raw material requirements and decreasing the volumes of waste to be managed.
This review discussed mine dump pollution monitoring and mine dump management strategies for some selected
countries. Impact and mechanism of mine damage to the environment was discussed together with the remediation
principles. It further examines the mining Act and regulations of the same selected countries. Emphasised
was placed on the enforcement of environmental laws, regulations, and standards. Practical ways in which
country’s state authority and civil society can keep a close watch and enhance the enforcement of laws and
regulations were highlighted. The prediction for the control of mineral exploration and environmental assessment
was also discussed for executing a specific control to take preventive measures. Management techniques used in
combating the impact of mine dump, stockpiles and tailing on the environment were discussed. In addition,
radioactivity in mine and its monitoring and control was discussed.
The management of long-lived mining wastes is a complex environmental challenge, but the subject is little discussed among the public, scientific community, and policymakers. The negative environmental impacts caused by mining wastes are severe and cause damage to human health and the loss and degradation of natural ecosystems. With the objective of stimulating discussion to advance the development of measures to contain threats to biodiversity and to mitigate negative impacts, we present an overview of total volumes of mining waste disposal in tailings dams and dump piles, discriminating them by ore type and biome. We highlight the major environmental risks and challenges associated with tropical forests, savannas, and freshwater ecosystems and possible limitations and advances in public policies and governance. The scale of this challenge is global, as some data show, for example, Brazil generated 3.6 billion tons of solid mining waste in dump piles in the period between 2008 and 2019. The volume is equivalent to 62% of the global mass of nonfuel minerals removed from the planet's crust in 2006. Numerous socio-environmental disasters are caused by catastrophic mining dam failures, and over the last 34 years, an average of one failure has occurred every three years in Brazil.
Introduction:
Impact of mining on environmental degradation affecting water, soil, and air is established. With the recognition that air pollution is the most important cause for noncommunicable cause for mortality and women as a gender at higher susceptibility to lung dysfunction, it was necessary to make a start to understand the detrimental effect of mine tailing on ambient air pollution almost two decades after closure and its effect on the vulnerable women residents.
Materials and methods:
After institutional ethical clearance and informed consent, 258 females between the age of 18 and 60 years living in the gold mining town for over 3 years were selected. Respiratory symptoms were assessed using the American Thoracic Society questionnaire and lung functions of forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), peak expiratory flow rate (PEFR), and FEV1/FVC% using computerized spirometer. Dust samples were analyzed for particulate matter concentration (PM) by gravimetric method.
Results:
The average PM concentration in the mining area was 1.491±0.737 mg/m. Of the respiratory symptoms, complaints of cough were 34%, breathlessness 31%, phlegm 30%, and asthma 20%. FVC, FEV1, and PEFR were 1.3157 ± 0.487 L/s, 1.2500 ± 0.4850 L/s, and 2.611 ± 1.185 L/s, respectively. FEV/FEV1 was 93.650 ± 9.2733%.
Conclusion:
Mine tailing contributed to ambient air pollution, which has significantly decreased lung functions in the local women residents and produced the restrictive type of lung abnormality.
In this study, a new paradigm containing motor observation, motor execution, and motor imagery was designed to investigate whether motor imagery (MI) and motor execution (ME) of finger gestures can be used to extend commands of practical mBCIs. The subjects were instructed to perform or imagine 30 left and right finger gestures. Hierarchical support vector machine (hSVM) method was applied to classify four tasks (i.e., ME and MI tasks between left and right gestures). The average classification accuracies of motor imagery and execution tasks using fivefold cross-validation were 90.89 ± 9.87% and 74.08 ± 13.42% in first layer and second layer, respectively. The average accuracy of classification of four classes is 83.06 ± 7.29% overall. These results show that performing or imaging finger movements have the potential to extend the commands of the existing BCI, especially for healthy elderly living.
Ecotoxicological studies are necessary in order to evaluate the effects of environmental exposure of chemicals on wild animals and their ecological consequences. Particularly, neurobehavioral effects of heavy metal elements on wild rodents have been scarcely investigated. In the present study, we analyzed the effect of metal bioaccumulation (Pb, As, Mg, Ni, and Zn) in the brain and in the liver on exploratory activity, learning, memory, and on some dopaminergic markers in the wild rodent Liomys irroratus living inside mine tailings, at Huautla, Morelos, Mexico. We found higher Pb concentration but lower Zn in striatum, nucleus accumbens, midbrain, and hippocampus in exposed animals in comparison to rodents from the reference site. Exposed rodents exhibited anxious behavior evaluated in the open field, while no alterations in learning were found. However, they displayed slight changes in the memory test in comparison to reference group. The neurochemical evaluation showed higher levels of dopamine and 5-hydroxyindolacetic acid in midbrain, while lower levels of metabolites dihydroxyphenyl acetic acid and homovanillic acid in striatum of exposed rodents. In addition, mRNA expression levels of dopaminergic D2 receptors in nucleus accumbens were lower in animals from the mining zone than in animals from the reference zone. This is the first study that shows that chronic environmental exposure to metals results in behavioral and neurochemical alterations in the wild rodent L. irroratus, a fact that may comprise the survival of the individuals resulting in long-term effects at the population level. Finally, we suggest the use of L. irroratus as a sentinel species for environmental biomonitoring of mining sites.
Web of Science and Scopus are two world-leading and competing citation databases. By using the Science Citation Index Expanded and Social Sciences Citation Index, this paper conducts a comparative, dynamic, and empirical study focusing on the use of Web of Science (WoS) and Scopus in academic papers published during 2004 and 2018. This brief communication reveals that although both Web of Science and Scopus are increasingly used in academic papers, Scopus as a new-comer is really challenging the dominating role of WoS. Researchers from more and more countries/regions and knowledge domains are involved in the use of these two databases. Even though the main producers of related papers are developed economies, some developing economies such as China, Brazil and Iran also act important roles but with different patterns in the use of these two databases. Both two databases are widely used in meta-analysis related studies especially for researchers in China. Health/medical science related domains and the traditional Information Science and Library Science field stand out in the use of citation databases.
Moss and lichen bag technique (Dicranum sp., Hypnum sp., Polytrichum sp., Hypogymnia physodes) and activity of soil enzymes (urease, acid and alkaline phosphatase, fluorescein diacetate, ß-glucosidase) were used as bioindicators of air and soil pollution in Dubník former mining area (East Slovakia). Ten open mining pits and 8 heaps of waste material were chosen for the research purposes. Contamination factor (C f), degree of contamination (C d), and pollution load index (PLI) were used to evaluate the level of soil pollution and relative accumulation factor (RAF) expressed the level of air pollution by risk elements (As, Cd, Cu, Fe, Mn, Ni, Sb, Pb, Zn). Based the degree of contamination results, the study area was polluted by individual elements in the following order: Fe>Cd>As>Pb>Sb>Zn>Cu>Ni>Mn. The highest values of Mn and Ni and lower values of urease were determined in open mining pits comparing heaps of waste material. The results of PLI index confirmed extreme pollution at all sampling sites. Considering the average RAF values showed the decrease of accumulation abilities of evaluated taxa in the following order: Dicranum sp.>Hypnum sp.>Hypogymnia physodes>Polytrichum sp.
The global mining industry produces billions of tonnes of mine tailings each year. This slurry of waste material is often contained in dams, which are among some of the world’s largest engineered structures. Several recent and catastrophic tailings dam disasters bring the complex interaction between a mine and its local operating context into plain sight. The absence of public, timely, multi-scalar information about the multiple dimensions of this interaction is a normalised feature in the management of tailings dams. This article highlights the importance of establishing and sharing diverse knowledge about tailings dam disaster risk. We argue that the assessment and disclosure of “situated” disaster risk ought to be prioritised; that is, the combined risk of a hazard bearing structure situated within a local context with inherent vulnerabilities. We present a method for examining situated disaster risk of tailings dams by utilising Environmental, Social and Governance (ESG) indicators to screen for risk across eight categories: waste, water, biodiversity, land uses, indigenous peoples’ lands, social vulnerability, political fragility, and approval and permitting. Applied to a global sample of operating mines, the method shows disaster risk potential of existing tailings dams globally. Future application could be used to generate a more complete inventory that includes both established and newly constructed facilities.
Purpose:
The medical literature relevant to germline genetics is growing exponentially. Clinicians need tools that help to monitor and prioritize the literature to understand the clinical implications of pathogenic genetic variants. We developed and evaluated two machine learning models to classify abstracts as relevant to the penetrance-risk of cancer for germline mutation carriers-or prevalence of germline genetic mutations.
Materials and methods:
We conducted literature searches in PubMed and retrieved paper titles and abstracts to create an annotated data set for training and evaluating the two machine learning classification models. Our first model is a support vector machine (SVM) which learns a linear decision rule on the basis of the bag-of-ngrams representation of each title and abstract. Our second model is a convolutional neural network (CNN) which learns a complex nonlinear decision rule on the basis of the raw title and abstract. We evaluated the performance of the two models on the classification of papers as relevant to penetrance or prevalence.
Results:
For penetrance classification, we annotated 3,740 paper titles and abstracts and evaluated the two models using 10-fold cross-validation. The SVM model achieved 88.93% accuracy-percentage of papers that were correctly classified-whereas the CNN model achieved 88.53% accuracy. For prevalence classification, we annotated 3,753 paper titles and abstracts. The SVM model achieved 88.92% accuracy and the CNN model achieved 88.52% accuracy.
Conclusion:
Our models achieve high accuracy in classifying abstracts as relevant to penetrance or prevalence. By facilitating literature review, this tool could help clinicians and researchers keep abreast of the burgeoning knowledge of gene-cancer associations and keep the knowledge bases for clinical decision support tools up to date.
Abstract Technologies and methods to speed up the production of systematic reviews by reducing the manual labour involved have recently emerged. Automation has been proposed or used to expedite most steps of the systematic review process, including search, screening, and data extraction. However, how these technologies work in practice and when (and when not) to use them is often not clear to practitioners. In this practical guide, we provide an overview of current machine learning methods that have been proposed to expedite evidence synthesis. We also offer guidance on which of these are ready for use, their strengths and weaknesses, and how a systematic review team might go about using them in practice.
The overwhelming majority of scientific knowledge is published as text, which is difficult to analyse by either traditional statistical analysis or modern machine learning methods. By contrast, the main source of machine-interpretable data for the materials research community has come from structured property databases1,2, which encompass only a small fraction of the knowledge present in the research literature. Beyond property values, publications contain valuable knowledge regarding the connections and relationships between data items as interpreted by the authors. To improve the identification and use of this knowledge, several studies have focused on the retrieval of information from scientific literature using supervised natural language processing3–10, which requires large hand-labelled datasets for training. Here we show that materials science knowledge present in the published literature can be efficiently encoded as information-dense word embeddings11–13 (vector representations of words) without human labelling or supervision. Without any explicit insertion of chemical knowledge, these embeddings capture complex materials science concepts such as the underlying structure of the periodic table and structure–property relationships in materials. Furthermore, we demonstrate that an unsupervised method can recommend materials for functional applications several years before their discovery. This suggests that latent knowledge regarding future discoveries is to a large extent embedded in past publications. Our findings highlight the possibility of extracting knowledge and relationships from the massive body of scientific literature in a collective manner, and point towards a generalized approach to the mining of scientific literature.
The Research Topic on “NLP-enhanced Bibliometrics” aims to promote interdisciplinary research in bibliometrics, Natural Language Processing (NLP) and computational linguistics in order to enhance the ways bibliometrics can benefit from large-scaletext analytics and sense mining ofpapers. The objectives of such research are to provide insights into scientific writing and bring new perspectives to the understanding of both the nature of citations and the nature of scientific papers and their internal structures. The possibility to enrich metadata by the full-text processing of papers offers a new field of investigation, where the major problems arise around the organization and structure of text, the extraction of information and its representation at the level of metadata.
Exposure to mine tailings dust from active and abandoned mining operations may be a very significant health hazard, especially to sensitive populations living in arid and semi-arid climates like the desert southwest of the US. It is anticipated that early life exposures during sensitive times of development can lead to adult disease. However, very few studies have investigated the effects of inhalation exposure to real world dusts during lung development. Using a mouse model, we have examined the effect(s) of inhalation of real world mine tailing dusts under three separate conditions: (1) Exposure only during in utero development (exposure of the pregnant moms) (2) exposure only after birth and (3) exposures that occurred continuously during in utero development, through gestation and birth until the mice reached adulthood (28 days old). We found that the most significant changes in lung structure and function were observed in male mice when exposure occurred continuously throughout development. These changes included increased airway hyper-reactivity, increased expression of epithelial to mesenchymal (EMT) transition protein markers and increased expression of cytokines related to eosinophils. The data also indicate that in utero exposures through maternal inhalation can prime the lung of male mice for more severe responses to subsequent postnatal exposures. This may be due to epigenetic alterations in gene regulation, immune response, molecular signaling, and growth factors involved in lung development that may make the neonatal lung more susceptible to continued dust exposure.
Despite citation counts from Google Scholar (GS), Web of Science (WoS), and Scopus being widely consulted by researchers and sometimes used in research evaluations, there is no recent or systematic evidence about the differences between them. In response, this paper investigates 2,448,055 citations to 2299 English-language highly-cited documents from 252 GS subject categories published in 2006, comparing GS, the WoS Core Collection, and Scopus. GS consistently found the largest percentage of citations across all areas (93%-96%), far ahead of Scopus (35%-77%) and WoS (27%-73%). GS found nearly all the WoS (95%) and Scopus (92%) citations. Most citations found only by GS were from non-journal sources (48%-65%), including theses, books, conference papers, and unpublished materials. Many were non-English (19%-38%), and they tended to be much less cited than citing sources that were also in Scopus or WoS. Despite the many unique GS citing sources, Spearman correlations between citation counts in GS and WoS or Scopus are high (0.78-0.99). They are lower in the Humanities, and lower between GS and WoS than between GS and Scopus. The results suggest that in all areas GS citation data is essentially a superset of WoS and Scopus, with substantial extra coverage.
Google Colaboratory (a.k.a. Colab) is a cloud service based on Jupyter Notebooks for disseminating machine learning education and research. It provides a runtime fully configured for deep learning and free-of-charge access to a robust GPU. This work presents a detailed analysis of Colaboratory regarding hardware resources, performance, and limitations. This analysis is performed through the use of Colaboratory for accelerating deep learning for computer vision and other GPU-centric applications. The chosen test-cases are a parallel tree-based combinatorial search and two computer vision applications: object detection/classification and object localization/segmentation. The hardware under the accelerated runtime is compared to a mainstream workstation and a robust Linux server equipped with 20 physical cores. Results show that the performance reached using this cloud service is equivalent to the performance of the dedicated testbeds, given similar resources. Thus, this service can be effectively exploited to accelerate not only deep learning but also other classes of GPU-centric applications. For instance, it is faster to train a CNN on Colaboratory’s accelerated runtime than using 20 physical cores of a Linux server. The performance of the GPU made available by Colaboratory may be enough for several profiles of researchers and students. However, these free-of-charge hardware resources are far from enough to solve demanding real-world problems and are not scalable. The most significant limitation found is the lack of CPU cores. Finally, several strengths and limitations of this cloud service are discussed, which might be useful for helping potential users.
La minería es considerada una de las actividades económicas que más produce contaminación a las fuentes hídricas y suelos, además de ser la explotación de un recurso finito en el cual se generan residuos estériles que terminan como escombros o vertidos en ríos. La responsabilidad y conciencia que han tomado las empresas y universidades sobre los daños causados ha hecho que se innove e invierta en tecnología que mejore la extracción y genere un sistema donde se pueda producir materiales sin usar químicos como cianuro y mercurio. En este artículo se presenta un trabajo realizado en la mina “Sociedad Minera la Elsy en Vetas, Santander” en conjunto con un grupo de investigación para diseñar un sistema sustentable efectivo de extracción y se muestra el estudio realizado a los residuos estériles para utilizarlos como agregados para construcción
Aqueous slurries’ dewatering using gravity-driven thickeners is an essential unit operation in mineral processing. Thickener feed dilution is often used for enhanced flocculation and sediment compressibility. This work investigated the effect of feed dilution on thickener performance for copper flotation tailings. Furthermore, the performance of the feed dilution system (eductor) was investigated from theoretical and practical perspectives. The results indicated that reducing eductor discharge solid loading increased thickener underflow solid loading. Higher thickener feed flow rate also affected eductor performance, reducing its discharge solid loading by 4 wt.%. Finally, a smart thickener dilution system was proposed to maintain optimum dilution.
Growing social awareness of the need to adequately treat mining waste in order to protect the environment has led to an increase in the research in this field. The aim of this study was to analyze the dynamics of the research focused on mining waste and its sustainable management on a worldwide scale from 1988 to 2017. A systematic review and a bibliometric analysis of 3577 articles were completed. The results show that research into mining waste has increased, with studies focusing on waste management accounting for almost 40% of the total. The most productive journals in this field were Applied Geochemistry and Science of the Total Environment. The five most productive countries were the United States, Canada, Spain, Australia, and China. Works on the sustainable management of mining waste were in the minority, but it is an area of research that has considerable potential given the growing social awareness of the environmental repercussions of mining activities and the demands for increasingly sustainable practices. The findings of this study could prove useful for studies into mine waste, as they depict a global view of this line of research.
The number of science publications is growing exponentially, thus increasing the need for understanding the knowledge base of various research streams and their emerging branches. From a social science perspective, the literature on the mining sector – the industrial sector that extracts ores and minerals from the ground – has also witnessed steady growth. However, this literature is rather fragmented in regards to the thematic topics and the geographical focus. To respond to this, this paper offers a systematic literature review of the social science research on the mining sector. The publication database of this review includes a set of 483 systemically selected papers from 976 authors, covering empirical research conducted in 73 countries from 5 continents: Africa, Europe, Asia, Australia and America. Our contribution is twofold. Firstly, we provide an analysis of the geography of the research in terms of both authorship and empirical focus. In terms of the geographical coverage of the empirical cases, Australia appears as the most studied country in the field, followed by countries in other regions such as Asia (China, India, Russia and Turkey), Africa (Ghana, South Africa and the Democratic Republic of the Congo), North America (the USA and Canada), Latin America (Brazil and Chile) and Europe (Poland, Spain and Sweden). However, this dispersion is not reflected in the geographical coverage of the affiliations of the authors. Secondly, we identify the most popular social science research topics on the mining sector. Our results show that the social science research on the mining sector shifted from the traditional research streams (e.g., industrialisation and growth, colonialization, technological and economic development, and the resource curse) to the new streams of research on social, environmental and economical sustainability (e.g., the social license to operate, corporate social responsibility, criticality of the rare earth elements, material flow analysis and environmental impacts). Overall, our study serves as an entry point for researches who are interested in social science research on the mining sector.
This article refers to a study in Tanzania on fringe benefits or welfare via the work contract1 where we will work both quantitatively and qualitatively. My focus is on the vital issue of combining methods or methodologies. There has been mixed views on the uses of triangulation in researches. Some authors argue that triangulation is just for increasing the wider and deep understanding of the study phenomenon, while others have argued that triangulation is actually used to increase the study accuracy, in this case triangulation is one of the validity measures. Triangulation is defined as the use of multiple methods mainly qualitative and quantitative methods in studying the same phenomenon for the purpose of increasing study credibility. This implies that triangulation is the combination of two or more methodological approaches, theoretical perspectives, data sources, investigators and analysis methods to study the same phenomenon.However, using both qualitative and quantitative paradigms in the same study has resulted into debate from some researchers arguing that the two paradigms differ epistemologically and ontologically. Nevertheless, both paradigms are designed towards understanding about a particular subject area of interest and both of them have strengths and weaknesses. Thus, when combined there is a great possibility of neutralizing the flaws of one method and strengthening the benefits of the other for the better research results. Thus, to reap the benefits of two paradigms and minimizing the drawbacks of each, the combination of the two approaches have been advocated in this article. The quality of our studies on welfare to combat poverty is crucial, and especially when we want our conclusions to matter in practice.
The impacts of the SAMARCO iron tailing spill along more than 650 km, between the dam and the plume of the Doce River in the Atlantic, were assessed by the determination of toxic metals. The tailing spill caused a substantial increase in suspended sediment loads (up to 33,000 mg L⁻¹), in addition to large depositions of waste along the Doce basin. The highest estimated transport of dissolved metals was observed for Fe (58.8 μg s⁻¹), Ba (37.9 μg s⁻¹) and Al (25.0 μg s⁻¹). Sediments reached the highest enrichment factors (EFs) for Hg (4,234), Co (133), Fe (43), and Ni (16), whereas As (55), Ba (64), Cr (16), Cu (17), Mn (41), Pb (38) and Zn (82) highest EFs were observed for suspended particulate matter (SPM). Iron, As, Hg, Mn exceeded sediment quality guidelines. Therefore, the risk of occurrence of adverse effects is highly possible, not only due to the dam failure, but also due to the Fe mining and the artisan Au mining. Heavy rain episodes will likely cause enhanced erosion, remobilization, and transport of contaminated particles, sustaining high inputs of SPM and metals for the years to come and threatening the ecosystem services.
After the collapse of the Fundão dam, 43 million m³ of iron ore tailings continue to cause environmental damage, polluting 668 km of watercourses from the Doce River to the Atlantic Ocean. The objectives of this study are to characterize the Fundão Tailings Dam and structural failures; improve the understanding of the scale of the disaster; and assess the largest technological disaster in the global context of tailings dam failures. The collapse of Fundão was the biggest environmental disaster of the world mining industry, both in terms of the volume of tailings dumped and the magnitude of the damage. More than year after the tragedy, Samarco has still not carried out adequate removal, monitoring or disposal of the tailings, contrary to the premise of the total removal of tailings from affected rivers proposed by the country's regulatory agencies and the worldwide literature on post-disaster management. Contrary to expectations, there was a setback in environmental legal planning, such as law relaxation, decrease of resources for regulatory agencies and the absence of effective measures for environmental recovery. It is urgent to review how large-scale extraction of minerals is carried out, the technical and environmental standards involved, and the oversight and monitoring of the associated structures.
Gold mining is a major source of metal and metalloid emissions into the environment. Studies were carried out in Krugersdorp, South Africa, to evaluate the ecological and human health risks associated with exposure to metals and metalloids in mine tailings contaminated soils. Concentrations of arsenic (As), cadmium (Cd), chromium (Cr), cobalt (Co), copper (Cu), lead (Pb), manganese (Mn), nickel (Ni), and zinc (Zn) in soil samples from the area varied with the highest contamination factors (expressed as ratio of metal or metalloid concentration in the tailings contaminated soil to that of the control site) observed for As (3.5x10²), Co (2.8x10²) and Ni (1.1x10²). Potential ecological risk index values for metals and metalloids determined from soil metal and metalloid concentrations and their respective risk factors were correspondingly highest for As (3.5x10³) and Co (1.4x10³), whereas Mn (0.6) presented the lowest ecological risk. Human health risk was assessed using Hazard Quotient (HQ), Chronic Hazard Index (CHI) and carcinogenic risk levels, where values of HQ > 1, CHI > 1 and carcinogenic risk values > 1×10⁻⁴ represent elevated risks. Values for HQ indicated high exposure-related risk for As (53.7), Cr (14.8), Ni (2.2), Zn (2.64) and Mn (1.67). Children were more at risk from heavy metal and metalloid exposure than adults. Cancer-related risks associated with metal and metalloid exposure among children were also higher than in adults with cancer risk values of 3×10⁻² and 4×10⁻² for As and Ni respectively among children, and 5×10⁻³ and 4×10⁻³ for As and Ni respectively among adults. There is significant potential ecological and human health risk associated with metal and metalloid exposure from contaminated soils around gold mine tailings dumps. This could be a potential contributing factor to a setback in the health of residents in informal settlements dominating this mining area as the immune systems of some of these residents are already compromised by high HIV prevalence.
Wind erosion, transport and deposition of windblown dust from anthropogenic sources, such as mine tailings impoundments, can have significant effects on the surrounding environment. The lack of vegetation and the vertical protrusion of the mine tailings above the neighboring terrain make the tailings susceptible to wind erosion. Modeling the erosion, transport and deposition of particulate matter from mine tailings is a challenge for many reasons, including heterogeneity of the soil surface, vegetative canopy coverage, dynamic meteorological conditions and topographic influences. In this work, a previously developed Deposition Forecasting Model (DFM) that is specifically designed to model the transport of particulate matter from mine tailings impoundments is verified using dust collection and topsoil measurements. The DFM is initialized using data from an operational Weather Research and Forecasting (WRF) model. The forecast deposition patterns are compared to dust collected by inverted-disc samplers and determined through gravimetric, chemical composition and lead isotopic analysis. The DFM is capable of predicting dust deposition patterns from the tailings impoundment to the surrounding area. The methodology and approach employed in this work can be generalized to other contaminated sites from which dust transport to the local environment can be assessed as a potential route for human exposure.
Each domain, along with its knowledge base, changes over time and every timeframe is centered on specific topics that emerge from different ongoing research projects. As searching for relevant resources is a time-consuming process, the automatic extraction of the most important and relevant articles from a domain becomes essential in supporting researchers in their day-to-day activities. The proposed analysis extends other previous researches focused on extracting co-citations between the papers, with the purpose of comparing their overall importance within the domain from a semantic perspective. Our method focuses on the semantic analysis of paper abstracts by using Natural Language Processing (NLP) techniques such as Latent Semantic Analysis, Latent Dirichlet Allocation or specific ontology distances, i.e., Word Net. Moreover, the defined mechanisms are enforced on two different sub domains from the corpora generated around the keywords "e-learning" and "computer". Graph visual representations are used to highlight the keywords of each sub domain, links among concepts and between articles, as well as specific document similarity views, or scores reflecting the keyword-abstract overlaps. In the end, conclusions and future improvements are presented, emphasizing nevertheless the key elements of our research support framework.
Development in manufacturing activities has shifted the industries towards the adoption of Industry 4.0 practices. Now the industries are adopting key enabling technologies of Industry 4.0 such as machine learning and artificial intelligence in the business practices and manufacturing activities to reduce the human effort and complete the customer demands in the minimum time. The adoption of CAD/CAM with the machine learning techniques in the design engineering has changed the market scenario as well as the business trends all over the world. Still the research is limited in this area. Industries are not aware about the current research progress in this area and there is no study which has mapped the research progress of machine learning in CAD/CAM and the discussed how industries can take benefit from this technology. This study aims at the study mapping of CAD/CAM research progress with machine learning in which we have discussed what has been done and what are some areas which still needs further research work. This study uses the Scopus database for the bibliographic data collection and bibliometrix R package for the analysis of data.
This article is dedicated to the improved approach for the finite-time stability (FTS) of fractional-order delayed Cohen-Grossberg mem-ristive neural networks (FDCGMNNs). First, a novel delayed integer-order Gronwall inequality is established. Second, on the basis of this inequality, a novel fractional-order delayed Gronwall inequality is developed. Third, a novel FTS criterion of FDCGMNNs is derived by virtue of the novelly developed fractional-order delayed Gronwall inequality. Eventually, the effectiveness and less conservativeness of the proposed results are shown by two numerical examples. ARTICLE HISTORY
Artificial intelligence (AI) is reshaping the economy, redefining the industry and the services sector. It is a disruptive technology, contributing to the development of different practices, new business models, and more efficient industrial processes. The literature highlights that AI will be included in all areas of organizations and the personal lives of citizens, and such dynamics are still significantly unstudied. The purpose of this research is to highlight the latest research about AI through a bibliometric analysis, which will include an up-to-date overview of artificial intelligence research on the goals, results, methodologies, and geographic distribution of studies during 2015–2019. AI applications to the industry and services have been growing over the last 5 years, as the research results show. The results include that most studies have concentrated on the effect of artificial intelligence on industries’ productivity potential. Robotics was the area of subject matter most researched. The findings show that the most common AI technologies are predictive analysis, machine learning, and robotics.
The objective of the study was to evaluate the impact of distractions on intracortical brain–computer interface (BCI) performance. Two individuals with tetraplegia had microelectrode arrays implanted into their motor cortex. Each used a BCI to move a robotic arm between two targets as quickly as possible, performed alone and with distractions. Performance outcomes were similar across conditions, except for a drop in the median path efficiency in the motor action distraction for Subject P2 (p = 0.033, Mann–Whitney U-test). Other distraction conditions resulted in intermittent impairments in performance, but were not statistically different from the no distraction condition. Both subjects rated the overall difficulty of the task with and without distractions as low. Consistent levels of BCI performance were mirrored by small differences in neural firing rates across conditions, demonstrating that experienced BCI users can maintain performance on a simple reaching task under these various conditions.
In this work, we conducted a visualized bibliometric analysis to map the research trends of machine learning in engineering (MLE) based on articles indexed in the Web of Science Core Collection published between 2000 and 2019. The research distributions, knowledge bases, research hotspots, and research frontiers for MLE studies are revealed by using VOSviewer software and visualization technology. The growth of the literature related to MLE averaged 24.3% in the past two decades. A total of 3057 peer-reviewed papers from 96 countries published in 1299 different journals were identified. The USA was the most productive country, with 23.73% of the overall articles and 32.25% of the overall citations. The most active research organization was MIT, with 41 publications and 1079 citations, and the Journal of Machine Learning Research had the largest number of citations in the field of MLE. In particular, our findings indicate that the research issues of “random forests”, “support vector machine”, “extreme learning machine”, “deep learning”, “statistical learning theory”, and “Python machine learning” formed the knowledge bases of MLE from 2000 to 2019, while the research hotspots focused on applications of machine learning benchmark algorithms. Burst detection analysis results showed that more burst keywords emerged and had a higher frequency of change after 2010. This study provides an insight view of the overall research trends of MLE and may help researchers better understand this research field and predict its dynamic directions.
Purpose
This paper presents a review of the existing state-of-the-art literature on machine learning (ML) in logistics and supply chain management (LSCM) by analyzing the current literature, contemporary concepts, data and gaps and suggesting potential topics for future research.
Design/methodology/approach
A systematic/structured literature review in the subject discipline and a bibliometric analysis were organized. Information regarding industry involvement, geographic location, research design and methods, data analysis techniques, university, affiliation, publishers, authors, year of publications is documented. A wide collection of eight databases from 1994 to 2019 were explored using the keywords “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract. A total of 110 articles were found, and information on a chain of variables was gathered.
Findings
Over the last few decades, the application of emerging technologies has attracted significant interest all around the world. Analysis of the collected data shows that only nine literature reviews have been published in this area. Further, key findings show that 53.8 per cent of publications were closely clustered on transportation and manufacturing industries and 54.7 per cent were centred on mathematical models and simulations . Neural network is applied in 22 papers as their exclusive algorithms. Finally, the main focuses of the current literature are on prediction and optimization , where detection is contributed by only seven articles.
Research limitations/implications
This review is limited to examining only academic sources available from Scopus, Elsevier, Web of Science, Emerald, JSTOR, SAGE, Springer, Taylor and Francis and Wiley which contain the words “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract.
Originality/value
This paper provides a systematic insight into research trends in ML in both logistics and the supply chain.
The lead–zinc areas of China have faced serious foulteousqulated heavy metal pollution. In this study, data on As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn concentrations in China’s lead–zinc mine tailings were collected and screened from published literature (2015–2020). The contamination assessments, geographical distributions, and health risk assessments of the eight heavy metals were analyzed. The results revealed that the mean concentrations of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn exceeded the corresponding background values for soils. Moreover, most of the lead–zinc mine tailing areas contaminated by heavy metals were located in the southern and eastern regions of China. The health risk assessment results indicated that oral ingestion was the main exposure route of heavy metals in the mine tailings, and children were more vulnerable to adverse effects. For a single metal, As and Pb presented high non-carcinogenic risks, and As and Cu presented the unacceptable carcinogenic risks. This study provides a timely analysis proving the urgent necessity of the treatment of heavy metal pollution in lead–zinc tailings in China.
Motor imagery (MI) is particularly attractive in brain-computer interface (BCI) in the sense that it does not need any external stimuli. However, the overall performance is often severely affected by subject’s mental states. In this study, a method based on common feature analysis (CFA) was proposed for MI electroencephalogram (EEG) patterns recognition, which can not only improve the recognition accuracy but also help to find reliable and interpretable features associated with specific MI patterns. Evaluation using several open competition datasets justifies that the common features could more accurately identify MI characteristics and hence substantially benefit MI EEG patterns recognition.
The present investigation aimed to understand dewatering amenability of the tailing slurry generated during the beneficiation of chromite ore. The tailing includes mainly ultrafine particles with a concentration of Cr⁺⁶ which causes environmental and storage issues. Several dewatering techniques were investigated, including settling by using two different thickeners (high rate thickening and paste thickening), pressure filtration, as well as classification by using hydrocyclone. This paper explains the results obtained in various stages of the dewatering process. Further, different dewatering circuits were examined for the effective dewatering of the tailing slurry, and the optimum solutions were discussed. It is also concluded that up to 97% of the water can be recovered and recycled from such tailing slurry by adopting an efficient dewatering circuit.
Techniques for dry processing low-grade iron ores and tailings are being investigated. Dry desliming tests using a rotating wheel air classifier and factorial design were performed on a difficult-to-treat low-grade high-goethite Australian iron ore tailings. The results were compared with theoretically ideal size separation and a hydrocyclone desliming study using the same tailings. The air classifier performance was poorer than the hydrocyclone due to agglomerated particles in the feed, including fines coating coarser particles. The "fish hook" effect was observed and discussed. After dry desliming, the silica and alumina contents of a selected product were 30% and 26% lower, respectively.
China is rich in titanium resources, which accounts for more than 35% of the world. But, until today, the industrial TiO2 recovery for ilmenite ore is slightly higher than 30% in the current pulsating high-gradient magnetic separation-flotation process (HGMS) so that a large amount of ilmenite value were lost in tailings. A centrifugal HGMS (CHGMS) method was introduced and a novel HGMS-flotation process combining the method was investigated to achieve an enhanced separation for Panzhihua ilmenite tailings in China. The results of this investigation indicate that this new process achieved a superior separation performance to the current one and produced a qualified ilmenite concentrate assaying 46.62% TiO2 at a significantly enhanced recovery reaching 40.42%. In the new process, the CHGMS separator concentrated ilmenites at high selectivity and produced a high-grade ilmenite concentrate fed to flotation cleaning process, which significantly benefited the enhanced separation for the ilmenite tailings. It was concluded that this new HGMS-flotation process is prospective in improving the separation performance for ilmenite from tailings.
Studies on ecological modernization have produced a fruitful discussion concerning the connection between society and nature based on the evolution of socio-political institutions. However, the question from the individual’s perspective and their evaluation of the environmental institutions’ modernization remains unsystematically explored. This paper introduces the actor’s narratives on what I called time and space restructuration to define a sensitive method to explore the socio-ecological controversies produced by the enactment of a new environmental institution at the national level. The paper reconstructs the social discourse in relation to the exponential growth of Chilean extractive industries, which run in parallel with the government’s intent to ensure high levels of sustainability through institutional changes that occurred between 1990 and 2015. The main argument proposes that rendering the ecological modernization as a final stage of institutional improvement obscures a controversial reorganization of actions and roles at the environmental level. From this perspective, the clash between different organizations of time and space at the environmental level, among different social actors, offers a valid perspective on the re-emergence of socio-ecological conflicts.
Quartz mill tailings are produced in a huge amount per year in China. Therefore, now there is an urgent demand for utilization of the quartz mill. In the present research, the quartz mill tailings was used as a filler for metakaolin-based geopolymer to restraint the shrinkage and improve mechanical property. This work studied the influence of quartz mill tailings on the properties of geopolymer prepared by alkali-activated metakaolin. Performance of geopolymer incorporated with quartz mill tailings were addressed in terms of workability, compressive and flexural strength, and drying shrinkage at different ages. Further, X-ray Diffraction, Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR) and Mercury Intrusion Porosimetry (MIP) were applied to investigate the relevant microstructure. The results indicated that addition of quartz mill tailings increased the viscosity of the paste, and meanwhile decreased the flowability of geopolymer. Up to 20 % of the tailings can replace metakaolin to prepare geopolymer without a negative effect on the mechanical property. Up to 30 % of quartz mill tailings had a role to decrease the drying shrinkage. Besides, weight loss ratio of geopolymer decreased gradually with increasing dosage of the quartz mill tailings. Addition of the quartz mill tailings did not change the mineral composition of alkali-activated metakaolin, and meanwhile the amorphous or semi-crystalline phase has been found. MIP and FTIR results showed that moderate addition of quartz mill tailings optimized the microstructure of geopolymer. Therefore, quartz mill tailings can replace metakaolin up to 20 % to prepare geopolymer.
Three moss (Pleurozium spp., Polytrichum spp., and Rhytidiadelphus spp.) and two lichen (Hypogymnia physodes and Pseudevernia furfuracea) taxons covered in the bags were used to monitor air quality. Bags were exposed at the different distances from the tailing pond because of insufficient security and source of heavy metal pollution. Moss/lichen bags were exposed for six weeks at 0-, 50-, 100-, 150- and 200-m distances from Slovinky tailing pond, in the main wind direction (down the valley). Accumulation ability of heavy metals expressed by relative accumulation factor (RAF) increases in the order of Polytrichum spp.<H. physodes <Pleurozium spp.<P. furfuracea <Rhytidiadelphus spp. Moss/lichen species showed different accumulation capacity for individual heavy metals. Rhytidiadelphus spp. was found to possess the significantly highest (P < 0.01) ability to accumulate Cd, Zn, Ni, Mn and Fe. The highest RAF values of Pb, Zn, Ni and Fe were determined in samples exposed at 200-m distance from pollution source.
This paper presents the comprehensive stability analysis of the rock-fill tailing dam constructed at Rampura Agucha zinc mine in Rajasthan, India. The results of the feasibility study carried out prior to the expansion of the dam height from existing 27 to 51 m have been presented. The final cross-section of the tailing dam was arrived based on the extensive stability analysis considering both the upstream and downstream methods of embankment rising. The factor of safety values was calculated from Bishop’s simplified method, Janbu’s method and Spencer’s method by considering the circular failure surfaces. Further, the dynamic stability analyses were also carried out using the pseudostatic approach. The results from the limit equilibrium approaches were validated with the shear strength reduction technique using finite difference-based Fast Lagrangian Analysis of Continua in 2D analysis. The factor of safety values calculated from the different approaches was found to be in good agreement with each other. ...
This study demonstrates an efficient synthesis of highly reactive silica using hydrometallurgical processing of serpentinite tailings. Proposed process uses two-stage (acid and alkaline) leaching of serpentinite tailings and precipitation of silica from the sodium metasilicate solution using hydrochloric acid. The alkaline leaching and the effect of impurities on the precipitation of amorphous silica under the conditions of maximum sol stability were examined in detail.The proposed route is technologically advantageous because the product did not contain residues of the original raw serpentinite and was characterised by high purity (99.4 wt. % SiO2), large specific area (541 m2 g−1) and consistent quality. Moreover, less sensitivity to the presence of impurities and longer gelation times, offering a longer time period for manipulating the product in the final stages of the process, were achieved. The total yield of silicon in the overall process was 90–91 %.
