Smart Manufacturing as a framework for Smart Mining

Based on the analogy between manufacturing and mining (i.e. ore 'production'), smart mining has four dimensions: (i) advanced digital-oriented technologies (such as Cloud computing and the Internet of things) with automated Cyber-Physical Systems (CPSs), adaptable production processes (dependent on working conditions) and production volume control (with optimal resource consumption); (ii) smart maintenance of CPSs; (iii) new ways for workers to perform their activities, using advanced digital-oriented technologies; and (iv) smart supply-chain (procurement of materials and spare parts / products delivery). This paper presents a case study on the smart mining approach implemented at a coal mining system in Serbia

Smart Manufacturing as a framework for Smart Mining

Based on the analogy between manufacturing and mining (i.e. ore 'production'), smart mining has four dimensions: (i) advanced digital-oriented technologies (such as Cloud computing and the Internet of things) with automated Cyber-Physical Systems (CPSs), adaptable production processes (dependent on working conditions) and production volume control (with optimal resource consumption); (ii) smart maintenance of CPSs; (iii) new ways for workers to perform their activities, using advanced digital-oriented technologies; and (iv) smart supply-chain (procurement of materials and spare parts / products delivery). This paper presents a case study on the smart mining approach implemented at a coal mining system in Serbia

Overcoming the Clinical–MR Imaging Paradox of Multiple Sclerosis: MR Imaging Data Assessed with a Random Forest Approach

BACKGROUND AND PURPOSE: In MS, the relation between clinical and MR imaging measures is still suboptimal. We assessed the correlation of disability and specific impairment of the clinical functional system with overall and regional CNS damage in a large cohort of patients with MS with different clinical phenotypes by using a random forest approach. MATERIALS AND METHODS: Brain conventional MR imaging and DTI were performed in 172 patients with MS and 46 controls. Cervical cord MR imaging was performed in a subgroup of subjects. To evaluate whether MR imaging measures were able to correctly classify impairment in specific clinical domains, we performed a random forest analysis. RESULTS: Between-group differences were found for most of the MR imaging variables, which correlated significantly with clinical measures ( r ranging from −0.57 to 0.55). The random forest analysis showed a high performance in identifying impaired versus unimpaired patients, with a global error between 7% (pyramidal functional system) and 31% (Ambulation Index) in the different outcomes considered. When considering the performance in the unimpaired and impaired groups, the random forest analysis showed a high performance in identifying patients with impaired sensory, cerebellar, and brain stem functions (error below 10%), while it performed poorly in defining impairment of visual and mental systems (error of 91% and 70%, respectively). In analyses with a good level of classification, for most functional systems, damage of the WM fiber bundles subserving their function, measured by using DTI tractography, had the highest classification power. CONCLUSIONS: Random forest analysis, especially if applied to DTI tractography data, is a valuable approach, which might contribute to overcoming the MS clinical−MR imaging paradox

Sandpile model on the Sierpinski gasket fractal

We investigate the sandpile model on the two-dimensional Sierpinski gasket fractal. We find that the model displays interesting critical behavior, and we analyze the distribution functions of avalanche sizes, lifetimes, and topplings and calculate the associated critical exponents τ=1.51±0.04, α=1.63±0.04, and μ=1.36±0.04. The avalanche size distribution shows power-law behavior modulated by logarithmic oscillations which can be related to the discrete scale invariance of the underlying lattice. Such a distribution can be formally described by introducing a complex scaling exponent [Formula Presented]≡τ+iδ, where the real part τ corresponds to the power law and the imaginary part δ is related to the period of the logarithmic oscillation