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Preconditions for successful implementation of the Finnish standard for sustainable mining
پیش شرط های اجرای موفقیت آمیز استاندارد فنلاند برای استخراج پایدار-2020 Mining companies are facing various environmental and social challenges, ranging from ecological concerns to community relations issues. In Finland, mining companies have responded to these challenges by developing a sustainability initiative to increase their responsibility. This study is based on a questionnaire administered to senior mine managers to capture their views on the matter. This research argues that for successful implementation of the ‘Towards Sustainable Mining Standard’, organizations need certain leadership and management processes to support implementation. This research also argues that organizations need a set of sustainability initiatives to guide their operations towards improved environmental performance. This research shows that sustainability initiatives play an important role in mining companies’ operations, and companies are committed to several initiatives in their businesses. Results show that top management commitment, adequate resource allocation, and a functioning management system with an emphasis on communication and stakeholder engagement are essential to support implementation. Keywords: Mining industry | Sustainability initiative | Towards sustainable mining | Mining standard | Implementation |
مقاله انگلیسی |
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Studies in the use of data mining, prediction algorithms, and a universal exchange and inference language in the analysis of socioeconomic health data
مطالعات در مورد استفاده از داده کاوی ، الگوریتم های پیش بینی و یک زبان تبادل جهانی و استنتاج در تجزیه و تحلیل داده های بهداشت اجتماعی اقتصادی-2019 While clinical and biomedical information in digital form has been escalating, it is socioeconomic factors that are
important determinants of health on the national and global scale. We show how collective use of data mining
and prediction algorithms to analyze socioeconomic population health data can stand beside classical correlation
analysis in routine data analysis. The underlying theoretical basis is the Dirac notation and algebra that is a
scientific standard but unusual outside of the physical sciences, combined with a theory of expected information
first developed for analyzing sparse data but still largely confined to bioinformatics. The latter was important
here because the records analyzed (which are for US counties and equivalents, not patients) are very few by
contemporary data mining standards. The approach is very unlikely to be familiar to socioeconomic researchers,
so the theory and the advantages of our inference nets over the Bayes Net are reviewed here, mostly using
socioeconomic examples. While our expertise and focus is in regard to novel analytical methods rather than
socioeconomics per se, a significant negative (countertrending) relationship between population health and
equity was initially surprising, at least to the present authors. This encouraged deeper exploration including that
of the relationship between our data mining methods and traditional Pearsons correlation. The latter is susceptible
to giving wrong conclusions if a phenomenon called Simpsons paradox applies, so this is also investigated.
Also discussed is that, even for very few records, associative data mining can still demand significant
computational resources due to a combinatorial explosion. Keywords: Population health | Socioeconomic | Data analytics | Data mining | Sparse data | Inference net | Hyperbolic Dirac net | Bayes net | Decision support |
مقاله انگلیسی |