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4IR Technologies to Boost Competitiveness of Africa’s Mining Industry – Energy Capital & Power

Set to define the future of mining, real-time data analytics provide additional insights into underlying mining systems and enable short interval control approaches for decision-making. Mining-specific solutions include tracking loads of materials being transported from mines, wear rates on critical mining tools to enable predictive maintenance, and computer vision applications, which all contribute towards developing a robust data engineering process and limiting the complexity of data management and warehousing.

In South Africa a leading producer of platinum, chrome, gold and diamonds globally ICT provider BCX has deployed 5G wireless-enabled technology to the Nungu Mine, enabling video monitoring and integrated connectivity to enhance the mines operational efficiencies and safety.

With evolving Environmental, Social, and Governance (ESG) standards, rising energy costs and limited infrastructure, mining firms operating in Africa are being compelled to innovate to improve efficiency and cut costs. Technologies such as AI and predictive analytics create actionable insights from raw data and offer the potential to reduce unplanned downtime, streamline processes, improve asset performance, and achieve more reliable and predictable outcomes for mining throughputs.

For example, ICT company Minetec Smart Mining has implemented collision avoidance devices across its operations in South Africa, with a view to reducing risk exposure and fatalities around mining operations.

Additionally, predictive analytic solutions and deep learning approaches can help diagnose equipment issues before failure and forecast an assets remaining lifetime. This technology is poised to enable mining companies to optimize maintenance schedules, thus allowing users to mitigate potential failures and maximize operational strategies.

Mining companies operating in Africa have already begun to embrace digitalization and 4IR technologies, taking advantage of the full spectrum of live metrics and data analytics to minimize their consumption of limited resources, reduce waste, improve transparency and ensure compliance.

In Mali, the Syama Underground Gold Mine developed by the Socit des Mines de Syama installed fiber-optic network connections to better manage and monitor mining activities. The Syama Mine is the worlds first purpose-built automated mine and has served to reduce costs by approximately 30% while improving overall efficiency. Meanwhile, in December 2021, diamond mining producer Debswana inaugurated its first 5G-oriented smart mine at the Jwaneng open-pit diamond mine in Botswana. The network solution provides stable connectivity while enabling interconnection among the mines production, safety and security systems.

Data analytics and predictive maintenance in mining is expected to reduce the planning time of operations by 20%-50%, while reducing overall operational costs by roughly 10%. Mining companies in Africa that embrace digitalization can save millions of dollars in averted asset failures, while improving consistency, performance and reliability. Meanwhile, the adoption of renewable energy systems can help the African mining sector reduce its environmental impact, with autonomous 4IR technologies poised to cut fuel consumption in processes such as loading, crushing and drilling.

Some of the continents most recent mines, such as the Kamoa-Kakula Copper Complex operated by diversified mining company, Ivanhoe Mines are being designed with automated equipment, allowing operators to work remotely, or semi-remotely, thereby vastly improving safety for miners.

The economic and environmental benefits of digitalization in the African mining sector will allow active mines to become more profitable and productive, while contributing to the reskilling of the local workforce through digital skills and training programs. Critical for the production of renewable energy technologies, such as solar panels, wind turbines, and electric vehicle batteries, the continent produces approximately 80% of the worlds platinum, 70% of global cobalt, 50% of its manganese, and a substantial amount of chromium, in addition to other critical resources such as copper, lithium, and rare earth elements.

Embracing available and future 4IR technologies will be imperative for the resilience and sustainability of the African mining industry, allowing the continent to drive ESG performance standards while ensuring its extractive sector remains competitive on the global stage.

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Metal-mining pollution impacts 23 million people worldwide – BBC

Updated 22 September 2023

Image source, Getty Images

An aerial view of a tailings dam storing waste from a copper-mining operation in Chile

At least 23 million people around the world live on flood-plains contaminated by potentially harmful concentrations of toxic waste from metal-mining activity, according to a study.

UK scientists mapped the world's 22,609 active and 159,735 abandoned metal mines and calculated the extent of pollution from them.

Chemicals can leach from mining operations into soil and waterways.

The researchers say future mines have to be planned "very carefully".

This is particularly critical as the demand surges for metals that will support battery technology and electrification, including lithium and copper, says Prof Mark Macklin from the University of Lincoln, who led the research.

"We've known about this for a long time," he told BBC News. "What's alarming for me is the legacy - [pollution from abandoned mines] is still affecting millions of people."

The findings, published in the journal Science, build on the team's previous studies of exactly how pollution from mining activity moves and accumulates in the environment.

The scientists compiled data on mining activity around the world, which was published by governments, mining companies and organisations like the US Geological Survey. This included the location of each mine, what metal it was extracting and whether it was active or abandoned.

Prof Macklin explained that the majority of metals from metal mining is bound up in sediment in the ground. "It's this material - eroded from mine waste tips, or in contaminated soil - that ends up in river channels or [can be] deposited over a flood-plain."

Prof Macklin and his colleagues used previously published field and laboratory analyses to work out how far this metal-contaminated sediment moves down river systems.

That data allowed the scientists to produce a computer model that could calculate the extent of river channels and flood-plains around the world that are polluted by mining waste - both from current and historical mining activity.

"We mapped the area that's likely to be affected, which, when you combine that with population data, shows that 23 million people in the world are living on ground that would be considered 'contaminated'," said Chris Thomas, who is professor of water and planetary health at the University of Lincoln.

"Whether those people will be affected by that contamination, we simply can't tell with this research, and there are many ways that people may be exposed," he stressed. "But there is agriculture and irrigation in many of those areas."

Crops grown on contaminated soils, or irrigated by water contaminated by mine waste, have been shown to contain high concentrations of metals.

Image source, PAul Brewer/University of Aberystwyth

Waste leaks downstream, after a dam partially fails at a mine in Romania

"Animals grazing on flood-plains may also eat contaminated plant material and sediment, especially after flooding, when fresh metal-rich sediment is deposited," the scientists explained in their paper.

"With climate change and more frequent floods," Prof Macklin added, "this legacy [pollution] is going to extend and expand."

Prof Jamie Woodward from the University of Manchester, who was not involved in the study, said the research highlighted the threat posed by "silent pollution" stored in flood-plains.

"A good deal of river monitoring is focused on water when the real 'nasties' are often associated with river sediments," he told BBC News.

"We need to better understand how contaminants are transported in the environment and where they are stored. This allows us to assess hazards and to mitigate against them. Heavily contaminated flood-plain grasslands should not be used for livestock grazing, for example."

The researchers point out in their study that metal mining represents "humankind's earliest and most persistent form of environmental contamination". Waste from mining began to contaminate river systems as early as 7,000 years ago.

Data visualisations by Kate Gaynor and Mike Hills

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Manifold-based sparse representation for opinion mining | Scientific … – Nature.com

Liu, Y. et al. Interaction-enhanced and time-aware graph convolutional network for successive point-of-interest recommendation in traveling enterprises. IEEE Trans. Ind. Inf. 19(1), 635643 (2022).

Article CAS Google Scholar

Qi, L. et al. Privacy-aware point-of-interest category recommendation in internet of things. IEEE Internet Things J. 9(21), 2139821408 (2022).

Article Google Scholar

Kang, D. & Yongtae, P. Review-based measurement of customer satisfaction in mobile service: Sentiment analysis and VIKOR approach. Expert Syst. Appl. 41(4), 10411050 (2014).

Article Google Scholar

Li, Y. M. & Li, T. Y. Deriving market intelligence from microblogs. Decis. Supp. Syst. 55(1), 206217 (2013).

Article Google Scholar

Rui, H., Liu, Y. & Whinston, A. Whose and what chatter matters? The effect of tweets on movie sales. Decis. Supp. Syst. 55(4), 863870 (2013).

Article Google Scholar

Karimi, Z. Opinion mining of Drug Reviews using Support Vector Machine for Multiple Instance Learning. In The 1st International and 3rd National Conference on Biomathematics (2022).

Caldo, D. et al. Machine learning algorithms distinguish discrete digital emotional fingerprints for web pages related to back pain. Sci. Rep. 13(1), 4654 (2023).

Article ADS CAS PubMed PubMed Central Google Scholar

Liu, Y. et al. a long short-term memory-based model for greenhouse climate prediction. Int. J. Intell. Syst. 37(1), 135151 (2022).

Article Google Scholar

Barzegar Gerdroodbary, M. Application of neural network on heat transfer enhancement of magnetohydrodynamic nanofluid. Heat Transf. Asian Res. 49(1), 197212 (2020).

Article Google Scholar

Ramezani, R., Maadi, M. & Khatami, S. M. A novel hybrid intelligent system with missing value imputation for diabetes diagnosis. Alex. Eng. J. 57(3), 18831891 (2018).

Article Google Scholar

Medhat, W., Hassan, A. & Korashy, H. Sentiment analysis algorithms and applications: A survey. Ain Shams engineering journal 5(4), 10931113 (2014).

Article Google Scholar

Karimi, Z., & Nasiri, K. Sentiment Analysis of Digikala Opinions using Adaptive Neuro-Fuzzy Inference System. In 4th International Conference on Soft Computing (2021).

Zhai, Z., Xu, H., Kang, B. & Jia, P. Exploiting effective features for chinese sentiment classification. Expert Syst. Appl. 38(8), 91399146 (2011).

Article Google Scholar

Hira, Z. M. & Gillies, D. F. A review of feature selection and feature extraction methods applied on microarray data. Adv. Bioinf. 1, 113 (2015).

Google Scholar

Gou, J. et al. Discriminative globality and locality preserving graph embedding for dimensionality reduction. Expert Syst. Appl. 144, 113079 (2020).

Article Google Scholar

Karimi, Z. & Shiry Ghidary, S. Semi-supervised classification in stratified spaces by considering non-interior points using Laplacian behavior. Neurocomputing 239, 223231 (2017).

Article Google Scholar

Karimi, Z. & Shiry Ghidary, S. Semi-supervised metric learning in stratified spaces via intergrating local constraints and information-theoretic non-local constraints. Neurocomputing 312, 165176 (2018).

Article Google Scholar

Wang, Y., Chen, S., Xue, H. & Fu, Z. Semi-supervised classification learning by discrimination-aware manifold regularization. Neurocomputing 147, 299306 (2015).

Article Google Scholar

Yang, B., Xiang, M. & Zhang, Y. Multi-manifold discriminant Isomap for visualization and classification. Pattern Recognit. 55, 215230 (2016).

Article ADS MATH Google Scholar

Elhamifar, E. & Vidal, R. Sparse manifold clustering and embedding. Adv. Neural Inf. Process. Syst. 24, 1 (2011).

Google Scholar

Zhao, G., Zhou, Z. & Zhang, J. Theoretical framework in graph embedding-based discriminant dimensionality reduction. Signal Process. 189, 108289 (2021).

Article Google Scholar

Zhao, G., Zhou, Z., Sun, L. & Zhang, J. Effective weight function in graphs-based discriminant neighborhood embedding. Int. J. Mach. Learn. Cybern. 14(1), 347360 (2023).

Article Google Scholar

Jahanbakhsh Gudakahriz, S., Eftekhari Moghadam, A. M. & Mahmoudi, F. Opinion texts clustering using manifold learning based on sentiment and semantics analysis. Sci. Program. 1, 115 (2021).

Google Scholar

Kim, K. & Lee, J. Sentiment visualization and classification via semi-supervised nonlinear dimensionality reduction. Pattern Recognit. 47(2), 758768 (2014).

Article ADS Google Scholar

Kim, K. An improved semi-supervised dimensionality reduction using feature weighting: Application to sentiment analysis. Expert Syst. Appl. 109, 4965 (2018).

Article Google Scholar

Li, J. Unsupervised robust discriminative manifold embedding with self-expressiveness. Neural Netw. 113, 102115 (2019).

Article PubMed MATH Google Scholar

Wright, J. et al. Sparse representation for computer vision and pattern recognition. Proc. IEEE 98(6), 10311044 (2010).

Article Google Scholar

Song, M., Chen, C., Bu, J. & Sha, T. Image-based facial sketch-to-photo synthesis via online coupled dictionary learning. Inf. Sci. 193, 233246 (2012).

Article Google Scholar

Yang, Y. et al. Expression transfer for facial sketch animation. Signal Process. 91(11), 24652477 (2011).

Article Google Scholar

Li, W., Zhang, J. & Dai, Q. H. Video denoising using shape-adaptive sparse representation over similar spatio-temporal patches. Signal Process.: Image Commun. 26(45), 250265 (2011).

Google Scholar

Jin, X., Wu, Y., Xu, Y. & Sun, C. Research on image sentiment analysis technology based on sparse representation. CAAI Trans. Intell. Technol. 7(3), 354368 (2022).

Article Google Scholar

Jain, P. K., Quamer, W., Pamula, R. & Saravanan, V. SpSAN: Sparse self-attentive network-based aspect-aware model for sentiment analysis. J. Ambient. Intell. Humaniz. Comput. 14(4), 30913108 (2023).

Article Google Scholar

Gu, X., Lu, L., Qiu, S., Zou, Q. & Yang, Z. Sentiment key frame extraction in user-generated micro-videos via low-rank and sparse representation. Neurocomputing 410, 441453 (2020).

Article Google Scholar

Karimi, Z., & Ramezani, R. Sparse Representation for Sentiment Analysis. In 2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS) (2020).

Dau, A., Salim, N., Rabiu, I. & Osman, A. Weighted aspect-based opinion mining using deep learning for recommender system. Expert Syst. Appl. 140, 112871 (2020).

Article Google Scholar

Kang, M., Ahn, J. & Lee, K. Opinion mining using ensemble text hidden Markov models for text classification. Expert Syst. Appl. 94, 218227 (2018).

Article Google Scholar

Kobayashi, N., Inui, K., Matsumoto, Y. Extracting aspect-evaluation and aspect-of relations in opinion mining. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), Prague (2007).

Somprasertsri, G., & Lalitrojwong, P. Automatic product feature extraction from online product reviews using maximum entropy with lexical and syntactic features. In 2008 IEEE International Conference on Information Reuse and Integration (2008).

Tan, S. & Zhang, J. An empirical study of sentiment analysis for chinese documents. Expert Syst. Appl. 34(4), 26222629 (2008).

Article Google Scholar

Ogura, H., Amano, H. & Kondo, M. Comparison of metrics for feature selection in imbalanced text classification. Expert Syst. Appl. 38(5), 49784989 (2011).

Article Google Scholar

Wang, S., Li, D., Song, X., Wei, Y. & Li, H. A feature selection method based on improved fishers discriminant ratio for text sentiment classification. Expert Syst. Appl. 38(7), 86968702 (2011).

Article Google Scholar

Tang, H. & Tang, C. X. A survey on sentiment detection of reviews. Expert Syst. Appl. 36(7), 1076010773 (2009).

Article Google Scholar

Abbasi, A., Chen, H. & Salem, A. Sentiment analysis in multiple languages: Feature selection for opinion classification in web forums. ACM Trans. Inf. Syst. (TOIS) 26(3), 134 (2008).

Article Google Scholar

Bai, X. Predicting consumer sentiments from online text. Decis. Support Syst. 50(4), 732742 (2011).

Article Google Scholar

Ye, Q., Zhang, Z. & Law, R. Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Syst. Appl. 36(3), 65276535 (2009).

Article Google Scholar

Cekik, R. & Uysal, A. K. A novel filter feature selection method using rough set for short text data. Expert Syst. Appl. 160, 113691 (2020).

Article Google Scholar

Koncz, P., & Paralic, J. An approach to feature selection for sentiment analysis. In 2011 15th IEEE International Conference on Intelligent Engineering Systems (2011).

Ahmad, S. R., Bakar, A. A., & Yaakub, M. R. Metaheuristic algorithms for feature selection in sentiment analysis. In 2015 Science and Information Conference (SAI) (2015).

Gokalp, O., Tasci, E. & Ugur, A. A novel wrapper feature selection algorithm based on iterated greedy metaheuristic for sentiment classification. Expert Syst. Appl. 146, 113176 (2020).

Article Google Scholar

Balakrishnan, P. V., Gupta, R. & Jacob, V. S. Development of hybrid genetic algorithms for product line designs. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 34(1), 468483 (2004).

Article Google Scholar

Liu, H. & Lei, Y. Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. Knowl. Data Eng. 17(4), 491502 (2005).

Article Google Scholar

Jun, S., Park, S.-S. & Jang, D.-S. Document clustering method using dimension reduction and support vector clustering to overcome sparseness. Expert Syst. Appl. 41(7), 32043212 (2014).

Article Google Scholar

Mao, Y., Balasubramanian, K., Lebanon, G. Dimensionality reduction for text using domain knowledge. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters, Stroudsburg, PA, USA (2010).

Ma, M., Deng, T., Ning, W. & Yanmei, C. Semi-supervised rough fuzzy Laplacian Eigenmaps for dimensionality reduction. Int. J. Mach. Learn. Cybern. 10, 397411 (2019).

Article Google Scholar

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Elucidating disease-associated mechanisms triggered by pollutants … – Physician’s Weekly

Despite well-documented effects on human health, the action modes of environmental pollutants are incompletely understood. Although transcriptome-based approaches are widely used to predict associations between chemicals and disorders, the molecular cues regulating pollutant-derived gene expression changes remain unclear. Therefore, we developed a data-mining approach, termed DAR-ChIPEA, to identify transcription factors (TFs) playing pivotal roles in the action modes of pollutants.Large-scale public ChIP-Seq data (human, n=15,155; mouse, n=13,156) were used to predict TFs that are enriched in the pollutant-induced differentially accessible genomic regions (DARs) obtained from epigenome analyses (ATAC-Seq). The resultant pollutant-TF matrices were then cross-referenced to a repository of TF-disorder associations to account for pollutant modes of action. We subsequently evaluated the performance of the proposed method using a chemical perturbation data set to compare the outputs of the DAR-ChIPEA and our previously developed differentially expressed gene (DEG)-ChIPEA methods using pollutant-induced DEGs as input. We then adopted the proposed method to predict disease-associated mechanisms triggered by pollutants.The proposed approach outperformed other methods using the area under the receiver operating characteristic curve score. The mean score of the proposed DAR-ChIPEA was significantly higher than that of our previously described DEG-ChIPEA (0.7287 vs. 0.7060; Q=5.27810; two-tailed Wilcoxon rank-sum test). The proposed approach further predicted TF-driven modes of action upon pollutant exposure, indicating that (1) TFs regulating Th1/2 cell homeostasis are integral in the pathophysiology of tributyltin-induced allergic disorders; (2) fine particulates (PM) inhibit the binding of C/EBPs, Rela, and Spi1 to the genome, thereby perturbing normal blood cell differentiation and leading to immune dysfunction; and (3) lead induces fatty liver by disrupting the normal regulation of lipid metabolism by altering hepatic circadian rhythms.Highlighting genome-wide chromatin change upon pollutant exposure to elucidate the epigenetic landscape of pollutant responses outperformed our previously described method that focuses on gene-adjacent domains only. Our approach has the potential to reveal pivotal TFs that mediate deleterious effects of pollutants, thereby facilitating the development of strategies to mitigate damage from environmental pollution. 2023. BioMed Central Ltd., part of Springer Nature.

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Global Coal Mining – 2023 Industry Report: Navigating the Coal Mines – GlobeNewswire

Dublin, Sept. 25, 2023 (GLOBE NEWSWIRE) -- The "Global Coal Mining - Industry Report" report has been added to ResearchAndMarkets.com's offering.

A comprehensive analysis of the global Coal Mining market has been released, offering valuable insights into the financial performance of the top 320 companies in the industry. This report, which includes detailed assessments of companies such as Adani Enterprises Limited, Ambey Mining Private Limited, and Asia Cement Corporation, covers key areas within the sector.

Market Overview

The global Coal Mining market analysis provides a thorough examination of the financial trends of the top 320 companies in the industry. This report is a valuable resource for individuals and organizations interested in:

Using an exclusive methodology, the report reveals that 151 companies have seen a decline in their financial ratings, while 43 have shown robust sales growth.

Individual Company Assessment

Each of the largest 320 companies has undergone meticulous scrutiny through individual assessments, utilizing the most current financial data. These assessments include:

Market Analysis

The report presents an in-depth 100-page analysis of the latest changes in the global Coal Mining market. This section includes:

The report equips busy managers with tools to monitor the financial well-being of their company, competitors, or potential acquisition targets. It aids in assessing the attractiveness of potential acquisitions, gaining a better understanding of the market, and identifying sound companies for trade partnerships.

Key Topics Covered:

The Coal Mining (Global) analysis is the most definitive and accurate study of the Coal Mining (Global) sector. It's split into two sections, employing both written and graphical analyses to evaluate the 320 largest Coal Mining (Global) companies. The report contains the most up-to-date financial data, applying these figures to create authoritative analyses.

Best Trading Partners: Identifying companies excelling in both sales and financial strength, such as Shougang Fushan Resources Group Limited.

Sales Growth Analysis: Reviewing the fastest-growing and fastest-shrinking companies, like Maheshwari Fuelchem Priavate Limited among the fastest-growing.

Profit Analysis: Analyzing gross profit and pre-tax profit over the last decade, alongside a profitability summary comparing industry profits against those of small, medium, and large companies.

Market Size: Comparing last year's market size with the most current figures, based on the largest 320 companies.

Rankings: Ranking the top 50 companies based on Market Share, Sales Growth, Gross Profit, and Pre-tax Profit.

Company Analysis: This section offers an in-depth analysis of the largest companies within the Coal Mining (Global) industry. Each business is examined using the Publisher's Model, providing The Publisher's Chart - a graphical representation measuring a company's ability to achieve sales growth while maintaining financial strength.

Conclusion

The global Coal Mining market analysis report delivers comprehensive financial insights into the industry's top companies. It serves as an essential resource for industry professionals, investors, and organizations seeking to navigate the coal mining market.

Companies Mentioned:

For more information about this report visit https://www.researchandmarkets.com/r/f89b8p

About ResearchAndMarkets.comResearchAndMarkets.com is the world's leading source for international market research reports and market data. We provide you with the latest data on international and regional markets, key industries, the top companies, new products and the latest trends.

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Crypto Mining in Tech Data Centers: Why It is Important? – Analytics Insight

The world of technology is constantly evolving, and with it, the demands on data centers are growing exponentially. One emerging trend that has been making waves in the tech industry is the integration of crypto mining into data centers.

Data centers are the backbone of the digital age, powering everything from cloud services to mobile apps. Crypto mining can optimize data center efficiency by utilizing excess computational power to mine cryptocurrencies. This not only generates additional revenue but also reduces energy wastage.

With the volatility of cryptocurrency markets, tech data centers can benefit from diversifying their revenue streams. Crypto mining provides an alternative source of income, making data centers less reliant on traditional revenue models.

Many crypto-mining operations are powered by renewable energy sources. By incorporating crypto mining into data centers, tech companies can promote eco-friendly practices and reduce their carbon footprint.

Crypto mining requires cutting-edge hardware and cooling solutions. Tech data centers that engage in crypto mining are at the forefront of technological innovation, pushing the boundaries of whats possible in the industry.

As the demand for cryptocurrencies continues to rise, so does the need for robust mining infrastructure. Tech data centers are well-positioned to meet this demand and provide essential support for the crypto ecosystem.

Tech data centers are known for their stringent security measures and reliability. This reputation extends to crypto mining operations, ensuring the safety of digital assets and transactions.

Crypto mining incentivizes the establishment of data centers in regions with abundant energy resources, including areas that were previously underserved. This expansion contributes to a more distributed and resilient internet infrastructure.

Incorporating crypto mining into tech data centers is not just a trend; its a strategic move that aligns with the evolving digital landscape. By enhancing efficiency, diversifying revenue streams, supporting renewable energy, fostering innovation, meeting growing demand, and ensuring security, tech data centers are playing a crucial role in the crypto ecosystem. As the world becomes increasingly reliant on digital technologies, the synergy between tech data centers and crypto mining will continue to shape the future of both industries.

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Uncovering the lack of awareness of sand mining impacts on … – Nature.com

Rentier, E. S. & Cammeraat, L. H. The environmental impacts of river sand mining. Sci. Total Environ. 838, 155877 (2022).

Article ADS CAS PubMed Google Scholar

Farahani, H. & Bayazidi, S. Modeling the assessment of socio-economical and environmental impacts of sand mining on local communities: A case study of Villages Tatao River Bank in North-western part of Iran. Resour. Policy 55, 8795 (2018).

Article Google Scholar

Chilamkurthy A. V. Chopperla M., K Marckson, S. T. S. A statistical overview of sand demand in Asia and Europe. Constr. Mater. Eng. Technol. Conf. 2016 Proc. 116 (2016).

Madyise, T. Case studies of environmental impacts of sand mining and gravel extraction for urban development in Gabarone. (2013).

Padmalal, D. & Maya, K. Impacts of River Sand Mining. in Sand Mining: Environmental Impacts and Selected Case Studies 3156 (2014).

Arsyad, A., Rukmana, D., Salman, D. & Alimuddin, I. Impact of sand mining on the changes of morphological and physical dynamics in Sadang River, Pinrang District, Indonesia. J. Degrad. Min. Lands Manag. 8, 24512460 (2020).

Article Google Scholar

Hiramatsu, A., Kurisu, K. & Hanaki, K. Environmental consciousness in daily activities measured by negative prompts. Sustainability 8, 24 (2016).

Article Google Scholar

THE 17 GOALS | Sustainable Development. Available at: https://sdgs.un.org/goals.

Anthony, E. J. et al. Linking rapid erosion of the Mekong River delta to human activities. Sci. Rep. 5, 14745 (2015).

Article ADS CAS PubMed PubMed Central Google Scholar

Tuyen, L. D. Mekong Delta pays a high price from sand mining. (2023). Available at: https://www.mekongeye.com/2023/05/01/mekong-delta-sand-mining/. Accessed 1 May 2023.

Identify the culprit that is causing deformation in the Mekong Delta | Dan Tri Newspaper. (2022).

Gruel, C.-R. et al. New systematically measured sand mining budget for the Mekong Delta reveals rising trends and significant volume underestimations. Int. J. Appl. Earth Obs. Geoinf. 108, 102736 (2022).

Google Scholar

An Giang handles riverbank landslides|An Giang Online Newspaper. (2022).

Many projects to overcome landslides need more funding|Can Tho Online Newspaper. (2021).

Ratang, S. Public perception toward the impact of people activities in sand and stone mining on economy and environment in Nulokla Village Jayapura. J. Educ. Vocat. Res. 8, 4548 (2017).

Article Google Scholar

Hammond, E. A. S. Effect of public perceptions on support/opposition of frac sand mining development. Extr. Ind. Soc. 6, 471479 (2019).

Google Scholar

Sari, I. K. & Sudarti, S. Analysis of community perception on the impact of sand mining in Mujur River and Regoyo River. SIGn J. Soc. Sci. 2, 112 (2021).

Article Google Scholar

Tri, V. P. D., Trung, P. K., Trong, T. M., Parsons, D. R. & Darby, S. E. Assessing social vulnerability to riverbank erosion across the Vietnamese Mekong Delta. Int. J. River Basin Manag. https://doi.org/10.1080/15715124.2021.2021926 (2022).

Article Google Scholar

Ahmed, S. N., Anh, L. H. & Schneider, P. A dpsir assessment on ecosystem services challenges in the Mekong delta, Vietnam: Coping with the impacts of sand mining. Sustainability 12, 129 (2020).

Google Scholar

Tran, N. N. Landslides in the Mekong Delta in the context of current challenges. (2017).

Hiep, H. V., Tri, H. H., Cong, N. T. & Truyen, N. G. Study of causes riverbanks erosion: Case study of Tra Vinh Province. Vietnam J. Hydrometeorol. 741, 1928 (2022).

Google Scholar

Binh, D. V., Kantoush, S. & Sumi, T. Changes to long-term discharge and sediment loads in the Vietnamese Mekong Delta caused by upstream dams. Geomorphology 353, 107011 (2020).

Article Google Scholar

List of mining areas operating in An Giang province | Department of Natural Resources and Environment of An Giang province. (2020).

List of mining licenses in Can Tho city|Department of Natural Resources and Environment of Can Tho city. (2020).

Kumar, R. Research Methodology A Step-by-Step Guide for Beginners. (Los Angeles SAGE, 2014, 2014).

Ha, T. P., Dieperink, C., Dang Tri, V. P., Otter, H. S. & Hoekstra, P. Governance conditions for adaptive freshwater management in the Vietnamese Mekong Delta. J. Hydrol. 557, 116127 (2018).

Article ADS Google Scholar

Socio-economic situation in the third quarter and 9 months of 2022Can Tho | Can Tho City Statistical Office (2022).

Socio-economic situation in September and 9 months of An Giang province in 2022|Statistical Office of An Giang province (2022).

Mngeni, A., Musampa, C. M. & Nakin, V. M. D. The effects of sand mining on rural communities. Sustain. Dev. Plan. VIII 1, 443453 (2016).

Google Scholar

Sada, R. & Shrestha, A. Report on state of sand mining at Peri-Urban Kathmandu: Case of Jhaukhel VDC. (2013).

Huang, Q. & Xu, J. Understanding the Power Interactions between Villages and the State: Insights from Sand Mining Issues. Asian J. Soc. Sci. 48, 567587 (2020).

Article ADS Google Scholar

Ashraf, M., Maah, M., Yusoff, I., Wajid, A. & Mahmood, K. Sand Mining effects, causes and concerns: A case study from Bestari Jaya, Selangor, Peninsular Malaysia. Sci. Res. Essays 6, 12161231 (2011).

Google Scholar

Ha Anh, H. & Thuy, N. Socio-economic assessment of riverbank erosion from heavy boat traffic: A case study at the Cho Gao Canal, Tien Giang, Vietnam. IOP Conf. Ser. Earth Environ. Sci. 967, 12005 (2022).

Article Google Scholar

Krzysztofik, R., Dulias, R., Kantor-Pietraga, I., Sprna, T. & Dragan, W. Paths of urban planning in a post-mining area: A case study of a former sandpit in southern Poland. Land Use Policy 99, 104801 (2020).

Article Google Scholar

Aliu, I. R., Akoteyon, I. S. & Soladoye, O. Sustaining urbanization while undermining sustainability: The socio-environmental characterization of coastal sand mining in Lagos Nigeria. GeoJournal 87, 52655285 (2022).

Article PubMed PubMed Central Google Scholar

Walsh, B., van der Plank, S. & Behrens, P. The effect of community consultation on perceptions of a proposed mine: A case study from southeast Australia. Resour. Policy 51, 163171 (2017).

Article Google Scholar

Wicaksana, A. S. & Amirudin, A. Community resistance against sand mining activities at the brown canyon Semarang. E3S Web Conf. 317, 1037 (2021).

Article Google Scholar

Arragon, L. van. Livelihoods Built On Sand: Exposing the Precarity of Labour in Cambodias Sand Extraction Industry. (University of Ottawa, 2021).

Sapkale, J. B. & Rathod, B. L. Perception based study of coastal sand dunes in the areas of Ratnagiri coasts, Maharashtra. Indian J. Sci. Technol. 9, 17 (2016).

Article Google Scholar

Ali, A. S. Socio: Economic Impacts of Sand Mining Activities in Zanzibar. (The Open University of Tanzania, 2020).

Delgado-Serrano, M. et al. Local perceptions on social-ecological dynamics in latin america in three community-based natural resource management systems. Ecol. Soc. 20, 24 (2015).

Article Google Scholar

Oh, S.-H., Lee, S. Y. & Han, C. The effects of social media use on preventive behaviors during infectious disease outbreaks: The mediating role of self-relevant emotions and public risk perception. Health Commun. 36, 972981 (2021).

Article PubMed Google Scholar

Pinter, N. & Rees, J. C. Assessing managed flood retreat and community relocation in the Midwest USA. Nat. Hazards 107, 497518 (2021).

Article Google Scholar

Huang, H., Huang, J., Liu, D. & He, Z. Understanding the public responses to landslide countermeasures in southwest China. Int. J. Disaster Risk Reduct. 64, 102500 (2021).

Article Google Scholar

Krishnamurthy, P. K. Disaster-induced migration: Assessing the impact of extreme weather events on livelihoods. Nat. Hazards Disaster Risk Reduct. https://doi.org/10.1080/17477891.2011.609879.11,96-111 (2012).

Article Google Scholar

Purnomo, M. et al. Resistance to mining and adaptation of Indonesia farmers household to economic vulnerability of small scale sand mining activities. Local Environ. https://doi.org/10.1080/13549839.2021.199023426,1498-1511 (2021).

Article Google Scholar

Perera, E. N. C., Jayawardana, D. T., Jayasinghe, P., Bandara, R. M. S. & Alahakoon, N. Direct impacts of landslides on socio-economic systems: A case study from Aranayake, Sri Lanka. Geoenviron. Disasters 5, 11 (2018).

Article Google Scholar

Boret, S. P. & Gerster, J. Social lives of tsunami walls in Japan: Concrete culture, social innovation and coastal communities. IOP Conf. Ser. Earth Environ. Sci. 630, 12029 (2021).

Article Google Scholar

Apergis, E. & Apergis, N. New evidence on corruption and government debt from a global country panel. J. Econ. Stud. 46, 10091027 (2019).

Article Google Scholar

Indraratna, B., Sathananthan, I., Bamunawita, C. & Balasubramaniam, A. S. Chapter 3: Theoretical and numerical perspectives and field observations for the design and performance evaluation of embankments constructed on soft marine clay. in (eds. Indraratna, B., Chu, J. & Rujikiatkamjorn, C. B. T.-G. I. C. H.) 83122 (Butterworth-Heinemann, 2015). https://doi.org/10.1016/B978-0-08-100192-9.00003-X

Engel, M., Boesl, F. & Brckner, H. Migration of Barchan dunes in qatar-controls of the shamal, teleconnections sea-level changes and Human Impact. Geosciences 8, 240 (2018).

Article ADS Google Scholar

McCright, A. M. The effects of gender on climate change knowledge and concern in the American public. Popul. Environ. 32, 6687 (2010).

Article Google Scholar

Abedin, M. A., Habiba, U. & Shaw, R. Gender and climate change: Impacts and coping mechanisms of women and special vulnerable groups. 165184. https://doi.org/10.1007/978-4-431-54249-0_10 (2013).

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Interventions against illegal mining have a spillover effect on legal … – MINING.com

Back in 2019, the Peruvian government deployed Operation Mercury (Operation Mercurio) in the La Pampa region, an area where gold mining is banned in most places. La Pampa straddles the Interoceanic Highway. North of the highway, mining is mostly legal in mining concessions. However, south of the highway mining is strictly prohibited in the buffer zone of the Tambopata National Reserve.

Through Operation Mercury, armed military and national police were dispatched to the region and had a sustained presence until March 2020. Miners were evicted and mining equipment was destroyed. The intervention successfully stopped illegal gold mining activity in La Pampa but activity in legal areas spiked, triggering many of the same environmental concerns.

Although illegal gold mining operations in La Pampa came to a near halt during Operation Mercurys two intervening years (20192020), mining activity essentially just shifted across the road to legal areas on the other side of the Interoceanic Highway, Dethier said.

Following Operation Mercury, mining decreased by 70% to 90%. Excavated mining pits in illegal mining areas decreased by up to 5% per year as compared to increasing by 33% to 90% per year before the intervention.

Although deforested areas experienced revegetation at a rate of 1 to 3 square kilometres per year, progress was offset by increases in deforestation in legal mining areas north of the Interoceanic Highway at a rate of 3 to 5 square kilometres per year. Most of the revegetation occurred on the edges of deforested areas, with the highest revegetation in La Pampa south. Mining pond areas outside intervention zones also saw increases ranging from 42% to 83%.

To assess Operation Mercurys impact on mining activity, the research team drew on satellite data from 2016 to 2021 from the European Space Agencys Sentinel-1 and Sentinel-2. Data were obtained from nine mining areas: four illegal mining areas targeted by the intervention, two legal areas to the north on the other side of the Interoceanic Highway, and three distant sites that were not part of the enforcement, which served as a control for the study.

Using the radar and multispectral data, the researchers were able to quantify changes in water, water quality, mining pond areas, and deforestation in La Pampa following Operation Mercury, by comparing data from before, during, and after the intervention.

As part of the analysis, the team examined the spectral properties of the mining ponds and changes in pond colour.

Mining ponds typically take on a yellow colour, which acts as a marker for gold mining activity. The yellowness of the ponds is associated with increases in suspended sediment in the water.

Through gold mining processes, sediment is churned up from the land, creating turbid water with lower reflectance levels, while clearer water has higher reflectance. After Operation Mercury was implemented, reflectance increased in mining ponds in La Pampa south but then stabilized.

Following Operation Mercury, pond yellowness decreased rapidly after mining activity was suspended in all areas of La Pampa, except in the north. In La Pampa northwest, mining activity spiked and pond yellowness increased by 43%, as compared to before the intervention. In La Pampa northeast, yellowness remained stable due to continued mining activity

Like many other countries around the world with highly prized natural resources, with Perus rich deposits of gold, it has had to determine who controls this extractable resource and how this particular mining sector will be formed, David A. Lutz, co-author of the paper that presents these findings, said.

By January 2023, when the paper was under review by the journal, illegal gold mining had resumed in protected areas, as enforcement and anticorruption activities by the military and national police had ceased, as they were redeployed to focus on the covid-19 pandemic.

Our results demonstrate how intervention at the federal level can effectively stop illegal mining in Peru, Dethier said. But that is just one aspect of the problem, as a multifaceted approach is necessary to address the long-term impacts of both illegal and legal gold mining activity on humans, wildlife and the environment in the Madre de Dios watershed.

In the researchers view, strong governance and conservation and remediation strategies are needed to protect this tropical biodiversity hotspot.

Dethier also mentioned that the same applies to protected areas in other counties, as another study he and Lutz co-authored showed the rise of similar mining operations in 49 countries across the global tropics.

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European markets in negative territory as investors assess interest rate decisions – CNBC

An Hour Ago

European markets opened lower, with all sectors in the red.

The pan-European Stoxx 600 index was down 0.3% in early trade, with all sectors in negative territory. Mining stocks led losses with a 1.9% drop, followed by travel and leisure, which fell 1.4%.

Hannah Ward-Glenton

8 Hours Ago

Goldman Sachs has forecast "healthy" growth in new lending at three major Indian banks over the next six months, which could lead to a significant upside for those stocks.

The Wall Street bank said Indian bank stocks have underperformed over the past three to six months despite a positive outlook for lending growth in the sector.

It named three lenders, that are expected to outperform the sector over the next 12 months, as "Top Buys".

CNBC Pro subscribers can read more here.

Ganesh Rao

8 Hours Ago

Portfolio manager Kamil Dimmich of North of South Capital says the word on the ground is that stocks in technology firm Nvidia are cheap even as he may personally not think so.

Shares in Nvidia tripled this year as the company's market valued topped $1 trillion over the optimism surrounding its artificial intelligence-powered applications.

Dimmich, who manages the $1.5 billion Pacific North of South Emerging Market All Cap Equity fund, says he is "always looking for great companies with strong cashflows that are not correctly reflected in the market." His focus is identifying undervalued stocks in emerging markets.

What is his list of 'great value' companies to watch?

CNBC Pro subscribers can read more here.

Amala Balakrishner

5 Hours Ago

European markets are expected to open in negative territory Monday.

The U.K.'s FTSE 100 index is expected to open 25 points lower at 7,666, Germany's DAX down 36 points at 15,526, France's CAC down 29 points at 7,161 and Italy's FTSE MIB down 74 points at 28,521, according to data from IG.

Data releases include Germany's closely watched Ifo Institute survey of business conditions for September.

Holly Ellyatt

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A digitalisation platform with an edge – Australian Mining

Improved decision-making is a primary benefit of digitalisation and this comes from having access to qualified data.

However, businesses are still looking to solve the problem of how that data is collected and processed so that it becomes informative and useful especially when there are large volumes of it.

According to Freddie Coertze, national IoT manager for ifm Australia, this is why the moneo digital solution can act as a middleware with an edge layer.

When we say middleware, were talking about a software layer that facilitates communication and integration between different applications, devices and systems, Coertze said.

And when we talk about edge computing in the context of collecting data and Internet of Things (IoT), were talking about processing large amounts of data near to where it is collected, which means its not clogging up networks or interfering with latency. Our digital solution, moneo, offers this it can serve as a middleware with a soft edge.

We have a large mining client who use our sensors to collect raw data, but they dont want to push that raw data directly into their higher level maintenance platform, as it would use up a lot of space and take them a lot of time to qualify that information.

With moneo, which has an in-built data science tool, it has the ability to filter the data, and with applied analytics on the edge, translates this data into readable and actionable information. Then it is sent to that upper platform. So in that context, moneo is acting as a middleware on the edge.

Its just more efficient, Coertze said. Edge computing helps businesses optimise operations by processing data near to the source rather than continuously sending it to a platform in the cloud.

It also adds a security layer which is important when we consider that most factory operations and automation systems havent been exposed to external threats before the event of digitalisation.

ifms moneo edgeConnect communication interface software can turn any PC into an edge device.

Basically this is a software component that will load onto a PC so it empowers you to make any PC an edge device. You could put these in several points around your facility or plant, Coertze said. It can qualify data near the source and send the results to the top platform.

In fact, moneo edgeConnect is designed to facilitate interoperability by building a bridge between production and automation systems (OT) with internal information systems and cloud platforms (IT).

This is a big deal for businesses, because it gives visibility across the whole operation from the shop floor to the top floor, Coertze said.

Importantly, moneo enables outgoing data transfer to the cloud platforms Amazon AWS and Microsoft Azure as well as third-part systems via an MQTT or OPC-UA server.

To learn more about the moneo platform pleaseclick here.Or to access a free trial, you canregister here.

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A digitalisation platform with an edge - Australian Mining

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