A large-scale shaking table test was performed to study the dynamic response of slopes parallel to geological bedding (bedding slopes) and slopes that cross-cut geological bedding (counter-bedding slopes). The test results show that the acceleration amplification coefficients increase with increasing elevation and, when the input earthquake amplitude is greater than 0.3 g, both bedding and counter-bedding slopes begin to show nonlinear dynamic response characteristics. With increasing elevation, the displacement of the bedding slope surface increases greatly. Conversely, the displacement of the counter-bedding slope surface increases first and then decreases; the slope develops a bulge at the relative elevation of 0.85. The displacement of the bedding slope surface is greater than that of the counter-bedding slope. The counter-bedding slope is more seismically stable compared with the bedding slope. Based on the Hilbert-Huang transform and marginal spectrum theories, the processes that develop dynamic damage of the bedding and counter-bedding slopes are identified. It is shown that the dynamic failure mode of the bedding slope is mainly represented by vertical tensile cracks at the rear of the slope, bedding slide of the strata along the weak intercalation, and rock collapse from the slope crest. However, the dynamic failure mode of the counter-bedding slope is mainly represented by staggered horizontal and vertical fissures, extrusion of the weak intercalation, and breakage at the slope crest.
Today's manufacturing systems are becoming increasingly complex, dynamic and connected. The factory operation faces challenges of highly nonlinear and stochastic activity due to the countless uncertainties and interdependencies that exist. Recent developments in Artificial Intelligence (AI), especially Machine Learning (ML) have shown great potential to transform the manufacturing domain through advanced analytics tools for processing the vast amounts of manufacturing data generated, known as Big Data. The focus of this paper is threefold: (1) Review the State-of-the-Art applications of AI to representative manufacturing problems, (2) Provide a systematic view for analyzing data and process dependencies at multiple levels that AI must comprehend, and (3) Identify challenges and opportunities to not only further leverage AI for manufacturing, but also influence the future development of AI to better meet the needs of manufacturing. To satisfy these objectives, the paper adopts the hierarchical organization widely practiced in manufacturing plants in examining the interdependencies from the overall system level to the more detailed granular level of incoming material process streams. In doing so, the paper considers a wide range of topics from throughput and quality, supervisory control in human robotic collaboration, process monitoring, diagnosis and prognosis, finally to advances in materials engineering to achieve desired material property in process modeling and control.
AbstractsBackgroundSevere fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by SFTS bunyavirus (SFTSV), a tick borne bunyavirus. However, Immunohistochemistry of SFTS patients are not well studied.MethodsWe obtained multiple of tissues from a fatal case with SFTS, including blood, lungs, kidneys, heart, and spleen. The blood samples were used to isolate the causative agent for detection of viral RNA and further expression of recombinant viral protein as primary antibody. Immunohistochemistry of the heart, lungs, spleen and kidneys was used to characterize the viral antigen in tissue sections.ResultsA 79-year-old man, together with his wife, was admitted because of fever. Both patients were diagnosed with SFTS by the positive SFTSV RNA in the blood. The gentleman died of multiple organ failure 8 days after hospitalization. However, his wife recovered and was discharged. Immunohistochemistry indicated that SFTSV antigens were present in all studied organs including the heart, kidney, lung and spleen, of which the spleen presented with the highest amount of SFTSV antigens. The kidney was next while the heart and lungs showed lower amount of SFTSV antigens.ConclusionsSFTSV can direct infect multiple organs, resulting in multiple organ failure and ultimately in an unfavorable outcome.Electronic supplementary materialThe online version of this article (10.1186/s12985-018-1006-7) contains supplementary material, which is available to authorized users.
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