The Industrial Internet of Things (IIoT), a concept that combines sensor networks and control systems, has been employed in several industries to improve productivity and safety. U.S. National Institute for Occupational Safety and Health (NIOSH) researchers are investigating IIoT applications to identify the challenges of and potential solutions for transferring IIoT from other industries to the mining industry. Specifically, NIOSH has reviewed existing sensors and communications network systems used in U.S. underground coal mines to determine whether they are capable of supporting IIoT systems. The results show that about 40 percent of the installed post-accident communication systems as of 2014 require minimal or no modification to support IIoT applications. NIOSH researchers also developed an IIoT monitoring and control prototype system using low-cost microcontroller Wi-Fi boards to detect a door opening on a refuge alternative, activate fans located inside the Pittsburgh Experimental Mine and actuate an alarm beacon on the surface. The results of this feasibility study can be used to explore IIoT applications in underground coal mines based on existing communication and tracking infrastructure.
An information metrics of soil texture -the Shannon Information Entropy-is proposed to analyze the contribution of the particle size distribution (PSD) in soil bulk density values.A database including 6239 soil samples from Florida is used. For each soil the Information Entropy is computed using mass proportions of the seven texture fractions that the database provides. The range interval of Information Entropy values is divided into an increasing number of subintervals of equal length, to study how differences in the soil texture metrics corresponded with differences in soil bulk density values. Grouping soil samples according to their information entropy, the average information entropy value in any of the resulting subintervals is plotted versus the corresponding average soil bulk density values. It was found that variations of less than 0.04 g/cm 3 in the mean bulk density values are explained with variations of mean information entropy values with an coefficient of determination equal to 0.99, being lower variations explained with no significant decrease in the fitting goodness. Predictions based in that linear regression give a mean predicted error (MPE) equal to 0.0015 g/cm 3 over the total number of soils, and a normal distribution of errors with standard deviation (SDPE) equal to 0.16 g/cm 3 . These results strongly support that Information Entropy serves as an indicator of the typical bulk density for a soil with a given PSD and average structure features.Information Entropy (IE) was also computed for all samples (6239) using clay, silt and sand content. Simple linear regression, now implemented using values of each soil sample, was used to predict bulk density values using the corresponding Information Entropy value as input. Additionally, different published bulk density pedotransfer functions (PTFs), including organic carbon (OC) content and texture inputs, are applied to the same data bank. Results show similar mean square predicting error (RSMPE) and standard deviation of predicted error (SDPE) when Information Entropy is used as unique input respect to those obtained using PTFs. The results become worse for soils in horizon A and better in horizon E, respectively, possibly due to the influence of the different OC content in those horizons. Notably, the MPE is, by average, about 3 3 orders of magnitude lower when IE is used respect to the MPE obtained by the PTFs, reflecting the potential of Information Entropy of texture in capturing mean soil bulk density values.Results show that Information Entropy metrics of soil texture provide a useful input for estimating bulk density, which also might be used together with other inputs as depth or OC content.
We have developed a new type of fragmentation algorithm that was inspired by a theoretical question raised by A.N. Kolmogorov—and still unanswered after 60 yr—regarding the characteristics of fragment size distributions when the size of the fragments, rβ, exhibits a power‐law dependence on the size of the original material, r, with 0 ≤ β ≤ 1. Our fragmentation algorithm uses β and N (which denotes the number of particles produced in the fragmentation) as input parameters and was used for various simulations performed with N values of 2, 3, and 4 and β values from 0 to 1. Simulations with β = 0 resulted in lognormal distributions according to the Kolmogorov–Smirnov goodness‐of‐fit test at a confidence level of 95%. On the other hand, simulations with fractional values of β > 0 gave highly heterogeneous distributions exhibiting multifractal characteristics. Rényi dimensions (Dq) and Hölder exponents [α(q)] at q = 0 and 1 were defined with coefficients of determination R2 > 0.95 in 78.3% of samples. The resulting α(0), box dimension D0, and entropy dimension D1 = α(1) values spanned the ranges 0.55 to 1.82, 0.52 to 1, and 0.48 to 0.94, respectively, and were thus suggestive of a multifractal nature in the simulated fragment size distributions. The multifractal characteristics of the simulated distributions were consistent with similar analyses performed on actual soil particle size distributions. These results suggest that the new algorithm can be useful for modeling natural fragmentation processes.
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