Search Results (3)

Commercial refrigeration systems currently utilize refrigerants with global warming potential (GWP) values ranging from 1250 to 4000. The advent of low GWP alternatives (GWP $<150$) is expected to significantly curtail direct emissions from this segment and greatly influence the ongoing electrification and decarbonization efforts. Most of the low GWP alternatives exhibit flammability risk and hence require robust sensing solutions for a reliable and safe operation of the equipment. This review article aims to provide an overview of different sensing mechanisms suitable for potential applications in systems employing flammable refrigerants, particularly those designated as A2L class. A summary of different A2L refrigerants and their properties is provided followed by a broad review of different classes of sensors, their working principle, transduction method, features, advantages, and limitations. Additionally, key performance characteristics of accuracy, selectivity, sensitivity, dynamic characteristic, and durability among other properties are discussed. Finally, areas of improvement and corresponding approaches are suggested for potential sensors in the successful adoption of A2L class refrigerants. Full article

Implantable Cardiovascular Therapeutic Devices (CTD), while lifesaving, impart supraphysiologic shear stress to platelets, resulting in thrombotic and bleeding coagulopathy. We previously demonstrated that shear-mediated platelet dysfunction is associated with downregulation of platelet GPIb-IX-V and αIIbβ3 receptors via generation of Platelet-Derived MicroParticles (PDMPs). Here, we test the hypothesis that sheared PDMPs manifest phenotypical heterogeneity of morphology and receptor surface expression and modulate platelet hemostatic function. Human gel-filtered platelets were exposed to continuous shear stress. Alterations of platelet morphology were visualized using transmission electron microscopy. Surface expression of platelet receptors and PDMP generation were quantified by flow cytometry. Thrombin generation was quantified spectrophotometrically, and platelet aggregation was measured by optical aggregometry. Shear stress promotes notable alterations in platelet morphology and ejection of distinctive types of PDMPs. Shear-mediated microvesiculation is associated with the remodeling of platelet receptors, with PDMPs expressing significantly higher levels of adhesion receptors (α_IIbβ₃, GPIX, PECAM-1, P-selectin, and PSGL-1) and agonist receptors (P₂Y₁₂ and PAR1). Sheared PDMPs promote thrombin generation and inhibit platelet aggregation induced by collagen and ADP. Sheared PDMPs demonstrate phenotypic heterogeneity as to morphology and defined patterns of surface receptors and impose a bidirectional effect on platelet hemostatic function. PDMP heterogeneity suggests that a range of mechanisms are operative in the microvesiculation process, contributing to CTD coagulopathy and posing opportunities for therapeutic manipulation. Full article

Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19’s informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naïve Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities. Full article