A review of non-destructive testing techniques for the in-situ investigation of fretting fatigue cracks
In: Materials and design, Band 196, S. 109093
ISSN: 1873-4197
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In: Materials and design, Band 196, S. 109093
ISSN: 1873-4197
In: Human factors: the journal of the Human Factors Society, Band 48, Heft 3, S. 526-539
ISSN: 1547-8181
Objective: The primary objective of this study was to create a methodology for measuring transient levels of physician workload in a live emergency department (ED) environment. Background: Characterizing, defining, and measuring aspects of this interrupt-driven work environment represent the preliminary steps in addressing impending issues concerning ED overcrowding, efficiency, and patient and provider safety. Methods: A time-motion task analysis was conducted. Twenty emergency medicine (EM) physicians were observed for 180-min intervals in an ED of an academic medical center. Near continuous workload measures were developed and used to track changing workload levels in time. These measures were taken from subjective, objective, and physiological perspectives. The NASA-Task Load Index was administered to each physician after observational sessions to measure subjective workload. Physiological measurements were taken throughout the duration of the observation to measure stress response. Additional information concerning physicians' patient quantity and patient complexity was extracted from the ED information system. Results: Graphical workload profiles were created by combining observational and subjective data with system state data. Methodologies behind the creation of workload profiles are discussed, the workload profiles are compared, and quantitative and qualitative analyses are conducted. Conclusion: Using human factors methods to measure workload in a setting such as the ED proves to be challenging but has relevant application in improving the efficiency and safety of EM. Application: Techniques implemented in this research are applicable in managing ED staff and real-time monitoring of physician workload.
Article in press ; Dynamic sitting, such as fidgeting and desk work, might be associated with health, but remains difficult toidentify out of accelerometry data. We examined, in a laboratory study, whether dynamic sitting can beidentified out of triaxial activity counts. Among 18 participants (56% men, 27.3 ± 6.5 years), up to 236 countsper minute were recorded in the anteroposterior and mediolateral axes during dynamic sitting using a hip-worn accelerometer. Subsequently, we examined in 621 participants (38% men, 80.0 ± 4.7 years) from theAGES-Reykjavik Study whether dynamic sitting was associated with cardio-metabolic health. Compared toparticipants who recorded the fewest dynamic sitting minutes (Q1), those with more dynamic sittingminutes had a lower BMI (Q2=−1.39 (95%CI =−2.33;–0.46); Q3=−1.87 (−2.82;–0.92); Q4=−3.38 (−4.32;–2.45)), a smaller waist circumference (Q2=−2.95 (−5.44;–0.46); Q3=−3.47 (−6.01;–0.93); Q4=−8.21 (−10.72;–5.71)), and a lower odds for the metabolic syndrome (Q2= 0.74 [0.45;1.20] Q3= 0.58 [0.36;0.95]; Q4=0.36[0.22;0.59]). Our findings suggest that dynamic sitting might be identified using accelerometry and that thisbehaviour was associated with health. This might be important given the large amounts of time peoplespend sitting. Future studies with a focus on validation, causation and physiological pathways are needed tofurther examine the possible relevance of dynamic sitting. ; This work was supported by the National Institute on Aging;National Institutes of Health [N01-AG-12100];FP7 People: Marie-Curie Actions (FP7- PEOPLE-2011-CIG) [PCIG09-GA-2011-293621];Icelandic Heart Association; Icelandic Parliament. ; Peer reviewed
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To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Download ; Dynamic sitting, such as fidgeting and desk work, might be associated with health, but remains difficult to identify out of accelerometry data. We examined, in a laboratory study, whether dynamic sitting can be identified out of triaxial activity counts. Among 18 participants (56% men, 27.3 ± 6.5 years), up to 236 counts per minute were recorded in the anteroposterior and mediolateral axes during dynamic sitting using a hip-worn accelerometer. Subsequently, we examined in 621 participants (38% men, 80.0 ± 4.7 years) from the AGES-Reykjavik Study whether dynamic sitting was associated with cardio-metabolic health. Compared to participants who recorded the fewest dynamic sitting minutes (Q1), those with more dynamic sitting minutes had a lower BMI (Q2 = -1.39 (95%CI = -2.33;-0.46); Q3 = -1.87 (-2.82;-0.92); Q4 = -3.38 (-4.32;-2.45)), a smaller waist circumference (Q2 = -2.95 (-5.44;-0.46); Q3 = -3.47 (-6.01;-0.93); Q4 = -8.21 (-10.72;-5.71)), and a lower odds for the metabolic syndrome (Q2 = 0.74 [0.45;1.20] Q3 = 0.58 [0.36;0.95]; Q4 = 0.36 [0.22;0.59]). Our findings suggest that dynamic sitting might be identified using accelerometry and that this behaviour was associated with health. This might be important given the large amounts of time people spend sitting. Future studies with a focus on validation, causation and physiological pathways are needed to further examine the possible relevance of dynamic sitting. ; National Institute on Aging National Institutes of Health FP7 People: Marie-Curie Actions (FP7-PEOPLE-2011-CIG) Icelandic Heart Association Icelandic Parliament
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In: https://ora.ox.ac.uk/objects/uuid:0cb75bde-dc77-465e-b576-6aea7eb1640e
In this manuscript, we firstly reviewed the challenges faced by China in its health care reform. Though Chinese governments have made tremendous efforts, problems like the difficulties and high expense in medical care and the nervous doctor-patient relationship have been reported a lot, whose key problem is the insufficiency of high-quality medical resource and the supply-demand imbalance. Presently, it's almost old news: artificial intelligence will overturn the existing medical model. Artificial intelligence technology will transform the medical sector and trigger an estimated $147 billion market during the next 20 years. We hereby pointed out the strengths of medical artificial intelligence and its potentials to relieve China's insufficient and unequally-distributed medical resources. Also, we analyzed China's advantages in developing medical AI due to its huge medical big data and China government's powerful promotion policy. Finally, we put forward some challenges for China to practice this.
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