An economy of knowledge: research, architectural practice and knowledge (in) translation

How does new knowledge 'flow' within an organisation? In this paper we report upon a case study in which ethnography is employed to render visible the 'knowledge transfer' (strategically redefined as 'knowledge translation') occurring between a PhD researcher and the members of the organisation in which he is 'embedded'. In this case the PhD student is located within an architectural firm and an industry context that is not accustomed to housing researchers in its midst. The path of knowledge flow, or rather its translation, is not found to be smooth. Knowledge 'flow' happens only in leaks and trickles through the organisation. We discuss the implications of this case for how ethnographic research in a business context might be communicated to an audience who do not necessarily value scrutiny of this nature

Improving the Performance of a Series-Parallel System Based on Lindley Distribution

In this article, the performance of a series-parallel system is improved. The system components are assumed to follows independently and identically Lindley distributed with three parameters. The system reliability for the given system will be improved by using reduction method, hot, cold and imperfect duplication method. Some reliability measures are derived. Two types of reliability equivalence factors and gamma fractiles are calculated. A numerical example is introduced to explain the theoretical results

The Effect of Anxiety Disorders on Smoking Cessation in Cancer Patients

https://openworks.mdanderson.org/sumexp23/1125/thumbnail.jp

Seismic Facies Classification And Identification By Competitive Neural Networks

We present an approach based on competitive networks for the classification and identification of reservoir facies from seismic data. This approach can be adapted to perform either classification of the seismic facies based entirely on the characteristics of the seismic response, without requiring the use of any well information, or automatic identification and labeling of the facies where well information is available. The former is of prime use for oil prospecting in new regions, where few or no wells have been drilled, whereas the latter is most useful in development fields, where the information gained at the wells can be conveniently extended to inter-well regions. Cross-validation tests on synthetic and real seismic data demonstrated that the method can be an effective means of mapping the reservoir heterogeneity. For synthetic data, the output of the method showed considerable agreement with the actual geologic model used to generate the seismic data, while for the real data application, the predicted facies accurately matched those observed at the wells. Moreover, the resulting map corroborates our existing understanding of the reservoir and shows substantial similarity to the low frequency geologic model constructed by interpolating the well information, while adding significant detail and enhanced resolution to that model.Saudi AramcoMassachusetts Institute of Technology. Borehole Acoustics and Logging ConsortiumMassachusetts Institute of Technology. Earth Resources Laboratory. Reservoir Delineation Consortiu

Evidence for Coupling of Velocity and Composition Fluctuations in a Binary Liquid Mixture

A critical mixture of isobutyric acid and water was quenched from the one-phase region into the two-phase region and, after the spinodal ring was well developed, a reverse quench returned the system to the one-phase region. Light-scattering measurements for this process exhibit a clearly nondiffusive relaxation which, at least for early times after the quench reversal, is in good agreement with Ruiz\u27s scheme for the coupling of velocity and composition fluctuations

Lung Transplantation in a Patient with COVID-19-Associated Acute Respiratory Failure

Coronavirus disease 2019 (COVID-19) is currently a significant cause of acute respiratory failure worldwide, leading to irreversible fibrotic lung disease. In patients with persistent respiratory failure after acute COVID-19 infection, lung transplant is an emerging option. Here, we have presented a case where the patient required venovenous extracorporeal membrane oxygenation (VV-ECMO) support for 33 days until a bilateral lung transplant was performed on day 71 after the initial COVID-19 infection. The early outcomes have been favorable. Currently, no guidelines exist for an acceptable time period after initial COVID-19 infection, duration of negative COVID polymerase chain reaction (PCR) testing, or negative Vero cell culture in the setting of persistent positive COVID PCR testing before listing for a lung transplant. Due to a lack of standardized guidelines, this patient was not listed for a lung transplant until the COVID-19 PCRs came negative on days 47 and 49 after the infection

Neural network edge detection and skin lesions image segmentation methods: analysis and evaluation

Similar to a human observer, an automated image vision system is able to recognise most parts of an object if the system could accurately trace and reflect its true shape. This has prompted the development of the many diverse edge detection techniques. Neural networks have been successfully applied to pattern recognition tasks and edge detection. However, there is a great necessity to analyse neural network models so as to achieve close insight into their internal functionality. To this purpose, a new and general training set, consisting of a limited number of prototype edge patterns, is proposed to analyse the problem of neural network edge detection. This thesis also proposes two approaches to the skin lesion image segmentation problem. The first is a mainly thresholding segmentation method where an optimal threshold is determined iteratively by an isodata algorithm. The second method proposed is based on neural network edge detection and a rational Gaussian curve that fits an approximate closed elastic curve between the recognized neural network edge patterns. A quantitative comparison of the techniques is enabled by the use of synthetic lesions to which Gaussian noise is added. The proposed techniques are also compared with an established automatic skin segmentation method. It is demonstrated that for lesions with a range of different border irregularity properties the thresholding segmentation method provides the best performance over a range of signal to noise ratios; the thresholding segmentation method is also demonstrated to have similar performance when tested on real skin lesions