An assessment of total RMR classification system using unified simulation model based on artificial neural networks

Engineering design has great importance in the cost and safety of engineering structures. Rock mass rating (RMR) system has become a reliable and widespread pre-design system for its ease of use and variety in engineering applications such as tunnels, foundations, and slopes. In RMR system, six parameters are employed in classifying a rock mass: uniaxial compressive strength of intact rock material (UCS), rock quality designation (RQD), spacing of discontinuities (SD), condition of discontinuities (CD), condition of groundwater (CG), and orientation of discontinuities (OD). The ratings of the first three parameters UCS, RQD, and SD are determined via graphic readings where the last three parameters CD, CG, and OD are estimated by the tables that are composed of interval valued linguistic expressions. Because of these linguistic expresions, the estimated rating values of the last three become fuzzy especially when the related conditions are close to border of any two classes. In such cases, these fuzzy situations could lead up incorrect rock class estimations. In this study, an empirical database based on the linguistic expressions for CD, CG, and OD is developed for training Artificial Neural Network (ANN) classifiers. The results obtained from graphical readings and ANN classifiers are unified in a simulation model (USM). The data obtained from five different tunnels, which were excavated for derivation purpose, are used to evaluate classification results of conventional method and proposed model. Finally, it is noted that more accurate and realistic ratings are reached by means of proposed model.     

Intersymbol interference channels with signal-dependent non-Gaussian noise: estimation of achievable information rates

In this letter, we calculate the achievable information rates for the intersymbol interference channels with general signal-dependent correlated noise under the constraint that the inputs are chosen from a finite alphabet. The proposed method employs canonical multivariate density estimation techniques. This method is important in that it can be used for accurate estimation of the achievable information rates for magnetic recording channels with media noise, where higher order media noise models can be taken into account instead of the first-order model.     

Innovative hydrogen release from sodium borohydride hydrolysis using biocatalyst-like Fe2O3 nanoparticles impregnated on Bacillus simplex bacteria

For the first time in this innovative study, microorganisms such as Bacillus simplex bacteria, mostly used in biological activity studies, are used as a bio-supporter agent of iron to release hydrogen from sodium borohydride hydrolysis at 25.0 ± 0.1 °C. The goal is to investigate thoroughly sodium borohydride hydrolysis catalyzed by Fe₂O₃ nanoparticles impregnated on microorganism such as Bacillus simplex (BS) bacteria (Fe₂O₃@BS NPs) known with strong antibacterial properties, which makes innovative them a candidate for hydrolysis reaction. This study was focused on the preparation, identification, and catalytic use of biocatalyst-like Fe₂O₃@BS NPs for hydrogen release from the sodium borohydride hydrolysis at 25.0 ± 0.1 °C. The characterization results made after and before hydrolysis reaction using by SEM/SEM-EDX, FT-IR, XRD, UV–vis, XPS, DLS, ELS Zeta potential, ESR, and TEM techniques reveal the formation of highly active, stable, durable, and long-lived biocatalysts-like Fe₂O₃@BS NPs.