Recovery of Cassiterite and Topaz Minerals from an Old Metallurgical Dump, Eastern Desert of Egypt

Huge amounts of tailing dumps as a result of mines’ blasting operations were impacting both economical and environmental problems. This issue was in serious need to be treated with suitable solutions. Evaluation of one of these tailing dumps in the Eastern Desert of Egypt showed the presence of reasonable amount of cassiterite mineral reaching 0.199%. The mineral was found as finely disseminated particulates within varieties of quartz-feldspar-hornblende-biotite granitic formations. In the present study, the processing regime relied upon the synergy between reaching liberation size, and mineral over grinding due to its extreme brittleness. However, delicate grinding via attrition scrubbing was adopted to produce − 0.51 + 0.074 mm attrition product with fine fractions, reaching 62.31% and 37.59%, respectively. The recovery of cassiterite from the − 0.50 + 0.074 mm size fraction was accomplished by the physical difference between mother granitic formations that shielded the mineral grains. Under these conditions, joint shaking table/dry high intensity magnetic separation techniques were conducted to recover cassiterite mineral. The CCD statistical system was used as a mathematical approach to optimize the effect of the main working parameters of the magnetic separator, i.e., splitter inclination angle, and belt speed, and their interactions on the cassiterite recovery of the final concentrate. The suggested flow sheet succeeded to recover cassiterite mineral with a grade reaching 11.25% SnO2 with 94.08% operational recovery from a feed contained 0.19% SnO2. These results are highly imperative to achieve applicable processing flow-sheet of such kind of minerals’ secondary resources.

SiGMoiD: A super-statistical generative model for binary data

In modern computational biology, there is great interest in building probabilistic models to describe collections of a large number of co-varying binary variables. However, current approaches to build generative models rely on modelers’ identification of constraints and are computationally expensive to infer when the number of variables is large (N~100). Here, we address both these issues with Super-statistical Generative Model for binary Data (SiGMoiD). SiGMoiD is a maximum entropy-based framework where we imagine the data as arising from super-statistical system; individual binary variables in a given sample are coupled to the same ‘bath’ whose intensive variables vary from sample to sample. Importantly, unlike standard maximum entropy approaches where modeler specifies the constraints, the SiGMoiD algorithm infers them directly from the data. Due to this optimal choice of constraints, SiGMoiD allows to model collections of a very large number (N>1000) of binary variables. Finally, SiGMoiD offers a reduced dimensional description of the data, allowing us to identify clusters of similar data points as well as binary variables. We illustrate the versatility of SiGMoiD using several datasets spanning several time- and length-scales.

How can we better coordinate and make use of statistical training resources? A few reflections linked to the work of the Global Network of Institutions on Statistical Training (GIST)1

Reliable and timely data and statistics are more important than ever before. Data are being used in many contexts, often without a proper understanding of what they mean. Having visible and active national statistics producers is key to help ensure that the public receives information that is reliable and can be used for informed decision making. While many official statistics producers do their best, particularly those that operate in low-resource settings have limited capacities and lack sufficient training to respond to all needs. A number of regional and international actors support statistical training, but provision is not always well coordinated or aligned with the prioritized needs of recipients. As a response to this, the Global Network of Institutions for Statistical Training (GIST) was established in 2018 with the aim to contribute to efficient, effective, and harmonized delivery of training. Since then GIST has developed various tools and guidance materials. Moving forward, the national statistical system should take a stronger lead and set their own priorities for training needs and coordinate with partners to fill gaps. The developments in technology and tools can support this change through increased use of online materials and therefore independence to use what is most relevant.

Systematic global evaluation of accuracy of seasonal climateforecasts for monthly precipitation of JMA/MRI-CPS2 bycomparing with a statistical system using climate indices

In this study, we aimed to evaluate the monthly precipitation forecasts of JMA/MRI-CPS2, a global dynamical seasonal climate forecast (Dyn-SCF) system operated in the Japan Meteorological Agency, by comparing them with the forecasts of a statistical SCF (St-SCF) system using climate indices systematically and globally. Accordingly, we developed a new global St-SCF system using 18 climate indices and compared the monthly precipitation of this system with those of JMA/MRI-CPS2. Consequently, it was found that JMA/MRI-CPS2 forecasts are superior to St-SCFs around the equator (10° S–10° N) even for six-month lead forecasts. For one-month lead forecasts, the accuracy of JMA/MRI-CPS2 forecasts was higher than that of St-SCFs when viewed globally. In contrast, for forecasts made two months or longer in advance, St-SCFs had an advantage in global forecasts. In addition to evaluating the accuracy of JMA/MRI-CPS2 forecasts, the slow dynamics of the ocean and atmosphere, not reproduced by the JMA/MRI-CPS2 system, were determined by comparing the evaluations, and it was concluded that this could contribute to improving Dyn-SCF systems.

The Influence of the Digitalization of the Banking System on the Transformation of Cash and Money Circulation in the Russian Federation

The purpose: to investigate the impact of digital technologies in the banking sector on the transformation of cash circulation in the Russian Federation.Methods: comparative-statistical, system, expert-analytical.Results: the reasons that determine the possibilities for expanding the use of non-cash payments are noted, the advantages of non-cash payments and QR payments are determined.Conclusions: the author substantiates the development and implementation of the digital economy and its impact on the control of cash flows of all counterparties of the financial system.