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The surface coating is one of the novel approaches to enhance the performance and durability of the mechanical components by decreasing the wear and friction among two interacting bodies. In this study, tribological and mechanical properties of titanium nitride (TiN) coatings were investigated on the AISI 52100 bearing steel deposited by low-temperature physical vapor deposition system. Surface morphology and elemental composition of the TiN coating were analyzed by scanning electron microscope and energy-dispersive X-ray spectrum, respectively. Substrate surface roughness and coating thickness of TiN were varied for correlative analysis among adhesion, mechanical, and tribological properties. Scratch and tribo tests were performed for evaluating the adhesion and tribological properties, respectively. Samples having the substrate surface roughness (0.2 ± 0.05 µm) and the coating thickness of more than 2.83 µm presented relatively better adhesion, wear resistance, and lower coefficient of friction of the TiN coating.

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Small non-coding RNAs (ncRNAs) are attracting increasing attention as they are now considered potentially valuable resources in the development of new drugs intended to cure several human diseases. A prerequisite for the development of drugs targeting ncRNAs or the related pathways is the identification and correct classification of such ncRNAs. State-of-the-art small ncRNA classification methodologies use secondary structural features as input. However, such feature extraction approaches only take global characteristics into account and completely ignore co-relative effects of local structures. Furthermore, secondary structure based approaches incorporate high dimensional feature space which is computationally expensive. The present paper proposes a novel Robust and Precise ConvNet (RPC-snRC) methodology which classifies small ncRNAs into relevant families by utilizing their primary sequence. RPC-snRC methodology learns hierarchical representation of features by utilizing positioning and information on the occurrence of nucleotides. To avoid exploding and vanishing gradient problems, we use an approach similar to DenseNet in which gradient can flow straight from subsequent layers to previous layers. In order to assess the effectiveness of deeper architectures for small ncRNA classification, we also adapted two ResNet architectures having a different number of layers. Experimental results on a benchmark small ncRNA dataset show that the proposed methodology does not only outperform existing small ncRNA classification approaches with a significant performance margin of 10% but it also gives better results than adapted ResNet architectures.

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