Digital pathology (DP) is being deployed in many pathology laboratories, but most reported experiences refer to public health facilities. In this paper, we report our experience in DP transition at a high-volume private laboratory, addressing the main challenges in DP implementation in a private practice setting and how to overcome these issues. We started our implementation in 2020 and we are currently scanning 100% of our histology cases. Pre-existing sample tracking infrastructure facilitated this process. We are currently using two high-capacity scanners (Aperio GT450DX) to digitize all histology slides at 40×. Aperio eSlide Manager WebViewer viewing software is bidirectionally linked with the laboratory information system. Scanning error rate, during the test phase, was 2.1% (errors detected by the scanners) and 3.5% (manual quality control). Pre-scanning phase optimizations and vendor feedback and collaboration were crucial to improve WSI quality and are ongoing processes. Regarding pathologists’ validation, we followed the Royal College of Pathologists recommendations for DP implementation (adapted to our practice). Although private sector implementation of DP is not without its challenges, it will ultimately benefit from DP safety and quality-associated features. Furthermore, DP deployment lays the foundation for artificial intelligence tools integration, which will ultimately contribute to improving patient care.

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The research disclosed in the present paper reports a new computer algorithm to maximize tool productivity in circular sawing processes, as a function of the stone characteristics and the quality required for the product. This algorithm is currently applied in tool testing at the company FrontWave and will be used in a new type of numeric machine to cut stone, called LeanMachine®. This optimization algorithm essentially depends on three variables: cutting depth, forward speed and rotational speed (identified as the main variables quantifying the sawing process), and how the variables are related with the forces acting on the tool. The algorithm uses the data provided by the relationships between each of the variables and the force acting on the tool (the so-called “force plots”) to determine the optimum working conditions for each tool, aiming to maximize productivity and minimize wear and energy consumption. The algorithm works with different production strategies, involving quality versus productivity, a key factor in the stone industry. A rating is subsequently attributed to each tool, allowing the establishment of tool rankings that can be used as selection criteria by machine operators or automatically in modern cutting stone machines such as LeanMachine®.

RESUMOO metal refratário nióbio aparece na indústria na década de 1930 como elemento de liga para melhorar a resistência dos aços inoxidáveis contra a corrosão intergranular. Por volta de 1950 o programa de desenvolvimento de materiais para aplicações nucleares e espaciais abordou nióbio como uma alternativa viável e interessante as aplicações tradicionais produzindo uma série de relatórios técnicas sobre propriedades mecânicas, físicas, químicas, de produção, refino e maquinabilidade. Uma maior diversificação das aplicações do material começa na década de 1970, onde o nióbio passa a ser utilizado em diversas aplicações tecnológicas, especialmente em super ligas para trabalho em altas temperaturas ganhando emprego em escala industrial. Na última década as aplicações de nióbio aumentaram constantemente em vários segmentos, aproveitando suas características: aços microligados, super ligas, implantes médicos, supercondutores e condensadores. Este artigo descreve a seleção de ferramental e parâmetros de usinagem para nióbio comercialmente puro e dá uma visão geral sobre a qualidade, melhor acabamento superficial possível e rendimento de ferramenta utilizada.