This chapter provides the theoretical basis for establishing an equivalent single-degree-of-freedom (ESDOF) approximation of the multi-degree-of-freedom (MDOF) system, and illustrates its use in estimating displacement response using nonlinear static (pushover) analysis. It presents an energy-based formulation of pushover analysis that is particularly useful for representing higher modes, and discusses the difficulties of estimating other response quantities. Nonlinear static analysis, also known as "pushover" analysis, is used to establish the load–displacement response of the system as it undergoes nonlinear response under a prescribed distribution of lateral forces. ESDOF estimates of peak displacement response generally will be close to but not coincide with the displacement response of the MDOF system. The chapter develops an alternative pushover method in which the ESDOF displacement coordinate is computed as an "energy-based" displacement. Nonlinear dynamic response history analyses represent the complicated interaction of multiple modes over time acting on structural components that may respond inelastically.
Celsus described four of the five cardinal signs of inflammation 2000 years ago, and Eustachio discovered the adrenal glands almost 500 years ago, but not until 1936 did Selye note that in rats exposed to stressors, the adrenal glands were enlarged, and the thymus and lymph nodes shrunken.13 Cortisone, the active principle of the adrenal glands, was isolated by Kendall and Reichstein in the late 1940s and shown to suppress immune organs. These scientists, along with Hench, received the Nobel Prize in Physiology and Medicine, after Hench and colleagues showed that cortisone could ameliorate rheumatoid arthritis.4,5 In recent . . .
SUMMARY The piecewise linear (‘multilinear’) approximation of realistic force‐deformation capacity curves is investigated for structural systems incorporating generalized plastic, hardening, and negative stiffness behaviors. This fitting process factually links capacity and demand and lies at the core of nonlinear static assessment procedures. Despite codification, the various fitting rules used can produce highly heterogeneous results for the same capacity curve, especially for the highly‐curved backbones resulting from the gradual plasticization or the progressive failures of structural elements. To achieve an improved fit, the error introduced by the approximation is quantified by studying it at the single‐degree‐of‐freedom level, thus avoiding any issues related to multi‐degree‐of‐freedom versus single‐degree‐of‐freedom realizations. Incremental dynamic analysis is employed to enable a direct comparison of the actual backbones versus their candidate piecewise linear approximations in terms of the spectral acceleration capacity for a continuum of limit‐states. In all cases, current code‐based procedures are found to be highly biased wherever widespread significant stiffness changes occur, generally leading to very conservative estimates of performance. The practical rules determined allow, instead, the definition of standardized low‐bias bilinear, trilinear, or quadrilinear approximations, regardless of the details of the capacity curve shape. Copyright © 2012 John Wiley & Sons, Ltd.
Background The effectiveness of public health measures depends upon a community’s compliance as well as on its positive or negative emotions. Objective The purpose of this study was to perform an analysis of the expressed emotions in English tweets by Greek Twitter users during the first phase of the COVID-19 pandemic in Greece. Methods The period of this study was from January 25, 2020 to June 30, 2020. Data collection was performed by using appropriate search words with the filter-streaming application programming interface of Twitter. The emotional analysis of the tweets that satisfied the inclusion criteria was achieved using a deep learning approach that performs better by utilizing recurrent neural networks on sequences of characters. Emotional epidemiology tools such as the 6 basic emotions, that is, joy, sadness, disgust, fear, surprise, and anger based on the Paul Ekman classification were adopted. Results The most frequent emotion that was detected in the tweets was “surprise” at the emerging contagion, while the imposed isolation resulted mostly in “anger” (odds ratio 2.108, 95% CI 0.986-4.506). Although the Greeks felt rather safe during the first phase of the COVID-19 pandemic, their positive and negative emotions reflected a masked “flight or fight” or “fear versus anger” response to the contagion. Conclusions The findings of our study show that emotional analysis emerges as a valid tool for epidemiology evaluations, design, and public health strategy and surveillance.
As peripheral blood mononuclear cells from patients with nocturnal asthma (NA) exhibit reduced steroid responsiveness at 4:00 a.m. as compared with 4:00 p.m., we hypothesized that NA is associated with increased nocturnal airway cell expression of GR β , an endogenous inhibitor of steroid action. Ten subjects with NA and seven subjects with nonnocturnal asthma (NNA) underwent bronchoscopy with bronchoalveolar lavage (BAL) at 4:00 p.m. and 4:00 a.m. BAL lymphocytes and macrophages were incubated with dexamethasone (DEX) at 10− 5 to 10− 8 M. DEX suppressed proliferation of BAL lymphocytes similarly at 4:00 p.m. and 4:00 a.m. in both groups. However, BAL macrophages from NA exhibited less suppression of IL-8 and TNF- α production by DEX at 4:00 a.m. as compared with 4:00 p.m. (p = 0.0001), whereas in the NNA group DEX suppressed IL-8 and TNF- α production equally at both time points. GR β expression was increased at night only in NA, primarily due to significantly increased expression by BAL macrophages (p = 0.008). IL-13 mRNA expression was increased at night, but only in the NA group and addition of neutralizing antibodies to IL-13 reduced GR β expression by BAL macrophages. We conclude that the airway macrophage may be the airway inflammatory cell driving the reduction in steroid responsiveness at night in NA, and this function is modulated by IL-13.