COVID-19 pandemic outbreak dictated the extensive use of personal protective equipment (PPE) by the majority of the population and mostly by frontline professionals. This need triggered a sudden demand that led to a global shortage of available PPEs threatening to have an immense contribution to the virus contamination spread. In these conditions, the need for a local, flexible, and rapid manufacturing method that would be able to cope with the increased demand for PPE fabrication arose. 3D printing proved to be such a manufacturing technique since its working principles make it an ideal technology for local, decentralized production of PPEs meeting the local demands. While considered to be more environmentally friendly than conventional fabrication techniques and aligning well with the principles of sustainability and circular economy, 3D printing can produce waste as the result of potential failed prints and material used for the fabrication of support structures. This paper describes the case of utilizing pre-existing FDM 3D printing equipment in an academic facility for the production of PPEs (face shields) and their distribution according to local demands. The plastic wastes produced were forwarded to a recycling process that led to their conversion to 3D filament that would be returned to the academic facility as raw material for future 3D printing operations. The followed procedure minimized 3D printing waste and led to a zerowaste fabrication case that was initiated in a pandemic for a greater-good cause (production of COVID-19 fighting PPEs) while assimilating the values of sustainability and circular economy.
Background and Objectives: Recent studies highlight the importance of investigating biomarkers for diagnosing and classifying patients with primary progressive aphasia (PPA). Even though there is ongoing research on pathophysiological indices in this field, the use of behavioral variables, and especially speech-derived factors, has drawn little attention in the relevant literature. The present study aims to investigate the possible utility of speech-derived indices, particularly silent pauses, as biomarkers for primary progressive aphasia (PPA). Materials and Methods: We recruited 22 PPA patients and 17 healthy controls, from whom we obtained speech samples based on two elicitation tasks, i.e., cookie theft picture description (CTP) and the patients’ personal narration of the disease onset and course. Results: Four main indices were derived from these speech samples: speech rate, articulation rate, pause frequency, and pause duration. In order to investigate whether these indices could be used to discriminate between the four groups of participants (healthy individuals and the three patient subgroups corresponding to the three variants of PPA), we conducted three sets of analyses: a series of ANOVAs, two principal component analyses (PCAs), and two hierarchical cluster analyses (HCAs). The ANOVAs revealed significant differences between the four subgroups for all four variables, with the CTP results being more robust. The subsequent PCAs and HCAs were in accordance with the initial statistical comparisons, revealing that the speech-derived indices for CTP provided a clearer classification and were especially useful for distinguishing the non-fluent variant from healthy participants as well as from the two other PPA taxonomic categories. Conclusions: In sum, we argue that speech-derived indices, and especially silent pauses, could be used as complementary biomarkers to efficiently discriminate between PPA and healthy speakers, as well as between the three variants of the disease.
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