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Motivation: This study deals with the introduction of artificial intelligence (AI) in digital pathology (DP). The study starts from the highlights of a companion paper. Objective: The aim was to investigate the consensus and acceptance of the insiders on this issue. Procedure: An electronic survey based on the standardized package Microsoft Forms (Microsoft, Redmond, WA, USA) was proposed to a sample of biomedical laboratory technicians (149 admitted in the study, 76 males, 73 females, mean age 44.2 years). Results: The survey showed no criticality. It highlighted (a) the good perception of the basic training on both groups, and (b) a uniformly low perceived knowledge of AI (as arisen from the graded questions). Expectations, perceived general impact, perceived changes in the work-flow, and worries clearly emerged in the study. Conclusions: The of AI in DP is an unstoppable process, as well as the increase of the digitalization in the health domain. Stakeholders must not look with suspicion towards AI, which can represent an important resource, but should invest in monitoring and consensus training initiatives based also on electronic surveys. [ABSTRACT FROM AUTHOR]

Polyoxymethylene dimethyl ethers (OMEn) are frequently discussed as alternative diesel fuels, with various synthesis routes considered. OME3–5 syntheses demand significant amounts of thermal energy due to the complex separation processes that they entail. Therefore, innovative process designs are needed. An important tool for the development of new processes is process simulation software. To ensure sound process simulations, reliable physico-chemical models and component property data are necessary. Herein we present the implementation of a state-of-the-art thermodynamic model to describe the component systems of formaldehyde-water and formaldehyde-methanol using Microsoft® Excel (2010, Microsoft Corp, Redmond, WA, USA) and Aspen Plus®, (V8.8, Aspen Tech, Bedford, MA, USA) determine the deviation between the calculated results and experimental literature data, and minimize the deviation by means of parameter fitting. To improve the accuracy of the estimation of the missing property data of hemiformals and methylene glycols formed from formaldehyde using group contribution methods, the normal boiling points were estimated based on molecular analogies. The boiling points of OME6-10 are determined through parameter regression in accordance with the vapor pressure equation. As an application example, an optimization of the product separation of the state-of-the-art formaldehyde synthesis is presented that helps decrease the losses of methanol and formaldehyde in flue gas and wastewater. [ABSTRACT FROM AUTHOR]