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To identify factors associated with distress experienced by physicians during their first COVID-19 triage decisions.
An online survey was administered to physicians licensed in New York State.
Of the 164 physicians studied, 20.7% experienced severe distress during their first COVID-19 triage decisions. The mean distress score was not significantly different between physicians who received just-in-time training and those who did not (6.0 ± 2.7 vs 6.2 ± 2.8, P=0.550) and between physicians who received clinical guidelines and those who did not (6.0 ± 2.9 vs 6.2 ± 2.7, P=0.820). Substantially increased odds of severe distress were found in physicians who reported that their first COVID-19 triage decisions were inconsistent with their core values (adjusted odds ratio 6.33, 95% confidence interval 2.03-19.76) and who reported having insufficient skills and expertise (adjusted odds ratio 2.99, 95% confidence interval 0.91-9.87).
About 1 in 5 physicians in New York experienced severe distress during their first COVID-19 triage decisions. Physicians with insufficient skills and expertise, and core values misaligned to triage decisions are at heightened risk of severe distress. Just-in-time training and clinical guidelines do not appear to alleviate distress experienced by physicians during their first COVID-19 triage decisions.
Cloud robotics (CR) is currently a growing area in the robotic community. Indeed, the use of cloud computing to share data and resources of distributed robotic systems leads to the design and development of cloud robotic systems (CRS) which constitute useful technologies for a wide range of applications such as smart manufacturing, aid and rescue missions. However, in order to get coherent agent-to-cloud communications and efficient agent-to-agent collaboration within these CRS, there is a need to formalize the knowledge representation in CR. Hence, the use of ontologies provides a mean to define formal concepts and their relations in an interoperable way. This paper presents standard robotic ontologies and their extension in the CR domain as well as their possible implementations in the case of a real-world CR scenario.
In 2017, the NYU Clinical and Translational Science Institute’s Recruitment and Retention Unit created a Patient Advisory Council for Research (PACR) to provide feedback on clinical trials and health research studies. We collaborated with our clinical research informatics team to generate a random sample of patients, based on the International Classification of Diseases, Tenth Revision codes and demographic factors, for invitation via the patient portal. This approach yielded in a group that was diverse with regard to age, race/ethnicity, sex, and health conditions. This report highlights the benefits and limitations of using an electronic health record-based strategy to identify and recruit members for a PACR.
This manuscript describes low-voltage epoxy-carbon nanotube composites with highly nonlinear resistances. Carbon nanotube paste was deposited on interdigitated electrodes and I-V characteristics were obtained over different voltage ranges and at different sweep speeds. In most cases, the injection process into the electrode-composite interface region was dominant, with exponential voltage dependence of the current.
Within the next decades, robots will need to be able to execute a large variety of tasks autonomously in a large variety of environments. To relax the resulting programming effort, a knowledge-enabled approach to robot programming can be adopted to organize information in re-usable knowledge pieces. However, for the ease of reuse, there needs to be an agreement on the meaning of terms. A common approach is to represent these terms using ontology languages that conceptualize the respective domain. In this work, we will review projects that use ontologies to support robot autonomy. We will systematically search for projects that fulfill a set of inclusion criteria and compare them with each other with respect to the scope of their ontology, what types of cognitive capabilities are supported by the use of ontologies, and which is their application domain.
The current fourth industrial revolution, or ‘Industry 4.0’ (I4.0), is driven by digital data, connectivity, and cyber systems, and it has the potential to create impressive/new business opportunities. With the arrival of I4.0, the scenario of various intelligent systems interacting reliably and securely with each other becomes a reality which technical systems need to address. One major aspect of I4.0 is to adopt a coherent approach for the semantic communication in between multiple intelligent systems, which include human and artificial (software or hardware) agents. For this purpose, ontologies can provide the solution by formalizing the smart manufacturing knowledge in an interoperable way. Hence, this paper presents the few existing ontologies for I4.0, along with the current state of the standardization effort in the factory 4.0 domain and examples of real-world scenarios for I4.0.
The properties of a mixed metallic and semiconducting carbon nanotube (CNT) sample dispersed in nonconjugated poly(methyl methacrylate) (PMMA) and conjugated poly(bisdodecylquaterthiophene) (PQT12) were compared, with and without p-doping by NOBF4. The CNTs were distributed much more evenly, and percolated at much lower concentrations (ca. 2%), in the PMMA as compared to PQT12, as judged by optical microscopy and electronic conductivity measurements. Seebeck coefficients (S) obtained on the PMMA samples indicated dominance by the metallic fraction, with values <10 µV/K. Composites made with PQT12 alone showed slightly higher values of S, but with the addition of 3 wt % dopant, S increased markedly to about 100 µV/K at 5-10% CNT fractions, while conductivity was unexpectedly low. As the CNT fraction in the doped sample was increased to 25-30%, conductivity approached that of the comparable concentration of CNTs in PMMA, while S, ca. 15 µV/K, was still higher than that measured in PMMA. The observations inform interpretations of CNT-polymer composite thermoelectric data, pointing out the roles of conjugated main chains and added dopants in modulating contributions of CNTs to thermoelectric composite performance.
Methylation of the fragile X mental retardation 1 (FMR1) exon 1/intron 1 boundary positioned fragile X related epigenetic element 2 (FREE2), reveals skewed X-chromosome inactivation (XCI) in fragile X syndrome full mutation (FM: CGG > 200) females. XCI skewing has been also linked to abnormal X-linked gene expression with the broader clinical impact for sex chromosome aneuploidies (SCAs). In this study, 10 FREE2 CpG sites were targeted using methylation specific quantitative melt analysis (MS-QMA), including 3 sites that could not be analysed with previously used EpiTYPER system. The method was applied for detection of skewed XCI in FM females and in different types of SCA. We tested venous blood and saliva DNA collected from 107 controls (CGG < 40), and 148 FM and 90 SCA individuals. MS-QMA identified: (i) most SCAs if combined with a Y chromosome test; (ii) locus-specific XCI skewing towards the hypomethylated state in FM females; and (iii) skewed XCI towards the hypermethylated state in SCA with 3 or more X chromosomes, and in 5% of the 47,XXY individuals. MS-QMA output also showed significant correlation with the EpiTYPER reference method in FM males and females (P < 0.0001) and SCAs (P < 0.05). In conclusion, we demonstrate use of MS-QMA to quantify skewed XCI in two applications with diagnostic utility.
This chapter reviews the results of the Salience Project, a cross-disciplinary research project focused on understanding how humans direct attention to salient stimuli. The first objective of the project was theoretical: that is, to understand behaviourally and electrophysiologically how humans direct attention through time to semantically and emotionally salient visual stimuli. Accordingly, we describe the glance-look model of the attentional blink. Notably, this model incorporates two levels of meaning, both of which are based upon latent semantic analysis, and, in addition, it incorporates an explicit body-state subsystem in which emotional experience manifests. Our second major objective has been to apply the same glance-look model to performance analysis of human–computer interaction. Specifically, we have considered a class of system which we call stimulus-rich reactive interfaces (SRRIs). Such systems are characterized by demanding (typically) visual environments, in which multiple stimuli compete for the user's attention, and a variety of physiological measures are employed to assess the user's cognitive state. In this context, we have particularly focused on electroencephalogram (EEG) feedback of stimulus perception. Moreover, we demonstrate how the glance-look model can be used to assess the performance of a variety of such reactive computer interfaces. Thus, the chapter contributes to the study of attentional support and adaptive interfaces associated with digital environments.
Humans are very good at prioritizing competing processing demands. In particular, perception of a salient environmental event can interrupt ongoing processing, causing attention, and accompanying processing resources, to be redirected to the new event.
Arid and semi-arid regions present special challenges for water management. They are, by definition, areas where water is at its most scarce, and face great pressures to deliver and manage freshwater resources. Demand for water has increased dramatically, due to population growth, increasing expectations for domestic water use, and expansion of industrial and agricultural activities. Available water resources have been reduced by pollution and over-abstraction. Many of the world's arid regions are further threatened by climate change. In addition, the science base to support water management remains limited. Hydrological processes can be very different from those of humid regions, precipitation and flow exhibit extreme variability in space and time, and data are often restricted in spatial coverage, record length and data quality.
UNESCO has identified, within the International Hydrological Programme, a special need to exchange knowledge on scientific aspects of water resources (with respect to both quantity and quality) in arid and semi-arid lands, and is supporting a number of regional centres to promote exchange of information and dissemination of good practice. At the global level, UNESCO has initiated G-WADI, a Global network for Water and Development Information for arid lands. Information on G-WADI products and a news-watch service can be found on the G-WADI website (www.gwadi.org). G-WADI aims to facilitate the global dissemination of state-of-the-art scientific knowledge and management tools, and to facilitate the sharing of scientific and technical knowledge and management experience of new and traditional technologies to conserve water.
Arid and semi-arid regions face major challenges in the management of scarce freshwater resources under pressures of population, economic development, climate change, pollution and over-abstraction. Groundwater is commonly the most important water resource in these areas. Groundwater models are widely used globally to understand groundwater systems and to guide decisions on management. However, the hydrology of arid and semi-arid areas is very different from that of humid regions, and there is little guidance on the special challenges of groundwater modelling for these areas. This book brings together the experience of internationally leading experts to fill a gap in the scientific and technical literature. It introduces state-of-the-art methods for modelling groundwater resources, illustrated with a wide-ranging set of illustrative examples from around the world. The book is valuable for researchers, practitioners in developed and developing countries, and graduate students in hydrology, hydrogeology, water resources management, environmental engineering and geography.