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The high-power impulse magnetron sputtering (HiPIMS) technique was applied to deposit multilayer-like (Cr, Y)Nx coatings on AISI 304L stainless steel, using pendular substrate oscillation and a Cr-Y target and varying the nitrogen flow rate from 10 to 50 sccm. The microstructure, mechanical and tribological properties were investigated by scanning and transmission electron microscopy, X-ray photoelectron spectroscopy, X-ray diffraction, instrumented nano-hardness, and wear tests. The columnar grain structure became highly segmented and nanosized due to pendular substrate oscillation and the addition of yttrium. The deposition rate increased continuously with the growing nitrogen flow rate. The increase in nitrogen flow from 10 to 50 sccm increased the hardness of the coatings (Cr, Y)Nx, with a maximum hardness value of 32.7 GPa for the coating (Cr, Y)Nx with a nitrogen flow of 50 sccm, which greatly surpasses the hardness of CrN films with multilayer-like (Cr, Y)Nx coatings architecture. The best mechanical and tribological performance was achieved for a nitrogen flow rate of 50 sccm. This was enabled by more elevated compressive stresses and impact energies of the impinging ions during film growth, owing to an increase of HiPIMS peak voltage with a rising N2/Ar ratio.

The mechanical and wear behavior of CrN/CrAlN multilayers were improved by tailoring the experimental conditions of a hybrid magnetron sputtering process based on a high-power impulse (HiPIMS) and two direct current magnetron sputtering (dcMS) power supplies. To this end, the influence of the base layer and of the combination of Cr and CrAl targets, which were switched to the dcMS and HiPIMS power supplies in different configurations, were investigated with respect to the growth of ceramic CrN/CrAlN multilayers onto commercial gas-nitrided diesel piston rings. The microstructure, grain morphology, and mechanical properties were evaluated by field emission scanning electron microscopy (FE-SEM), atomic force microscopy (AFM), X-ray diffraction (XRD), and instrumented nanoindentation. Bench wear tests simulating the operation of a combustion engine were conducted against a gray cast iron cylinder liner under reciprocating conditions using 0W20 oil as a lubricating agent enriched with Al2O3 particles. The results revealed a significant increase in hardness, resistance to plastic strain, and wear resistance when two CrAl targets were switched to a HiPIMS and a dcMS power supply, and a Cr target was powered by another dcMS power supply. The compressive coating stresses were slightly reduced due to the soft Cr base layer that enabled stress relief within the multilayer. The proposed concept of hybrid magnetron sputtering outperformed the commercial PVD coatings of CrN for diesel piston rings manufactured by cathodic arc evaporation.

Introduction: This study investigates whether it is possible to predict a final diagnosis based on a written nephropathological description—as a surrogate for image analysis—using various NLP methods. Methods: For this work, 1107 unlabelled nephropathological reports were included. (i) First, after separating each report into its microscopic description and diagnosis section, the diagnosis sections were clustered unsupervised to less than 20 diagnostic groups using different clustering techniques. (ii) Second, different text classification methods were used to predict the diagnostic group based on the microscopic description section. Results: The best clustering results (i) could be achieved with HDBSCAN, using BoW-based feature extraction methods. Based on keywords, these clusters can be mapped to certain diagnostic groups. A transformer encoder-based approach as well as an SVM worked best regarding diagnosis prediction based on the histomorphological description (ii). Certain diagnosis groups reached F1-scores of up to 0.892 while others achieved weak classification metrics. Conclusion: While textual morphological description alone enables retrieving the correct diagnosis for some entities, it does not work sufficiently for other entities. This is in accordance with a previous image analysis study on glomerular change patterns, where some diagnoses are associated with one pattern, but for others, there exists a complex pattern combination.

In this article we investigate whether a custom or an established biomedical terminology is recommended for coding cancer datasets. We first give an overview of biomedical terminology focused on the domain of cancer and introduce three clinical use cases to demonstrate how several cancer aspects can be coded using ICD-10, ICD-O, TNM, MeSH, NCIt, MedDRA, and SNOMED CT. The same collection of terminologies was used in a case study where two dimensions of cancer (anatomy and histology) had already been coded in a dataset using a custom terminology. Although our experiments were limited in terms of coders (2) and coding cases (250 in total, 50 double-coded), they showed that, in most cases, equivalent concepts already existed in standard biomedical terminologies. SNOMED CT and NCIt provided the highest coverage (88% vs. 93%), with NCIt showing a much higher agreement (unweighted Kappa of 71% vs. 49%). As a general conclusion, for annotating cancer datasets we recommend the use of standard terminologies and mappings to local interface terminologies or value sets, instead of building a custom terminology from scratch. Keywords: Biomedical terminology, Cancer, Annotation, SNOMED CT, NCIt