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“In Malaya,” theDaily Mailnoted in 1953, “three and a half years of danger have given the planters time to convert their previously pleasant homes into miniature fortresses, with sandbag parapets, wire entanglements, and searchlights.” The image of the home as fortress and a juxtaposition of the domestic with menace and terror were central to British media representations of colonial wars in Malaya and Kenya in the 1950s. The repertoire of imagery deployed in theDaily Mailfor the “miniature fortress” in Malaya was extended to Kenya, where the newspaper noted wire over domestic windows, guns beside wine glasses, the charming hostess in her black silk dress with “an automatic pistol hanging at her hip.” Such images of English domesticity threatened by an alien other were also central to immigration discourse in the 1950s and 1960s. In the context of the decline of British colonial rule after 1945, representations of the empire and its legacy—resistance to colonial rule in empire and “immigrants” in the metropolis—increasingly converged on a common theme: the violation of domestic sanctuaries.Colonial wars of the late 1940s and 1950s have received little attention in literatures on national identity in early postwar Britain, but the articulation of racial difference through immigration discourse, and its significance in redefining the postimperial British national community has been widely recognized. As Chris Waters has suggested in his work on discourses of race and nation between 1947 and 1963, these years saw questions of race become central to questions of national belonging.

The scheduling of operating room (OR) slots requires the accurate prediction of surgery duration. We evaluated the performance of existing Moving Average (MA) based estimates with novel machine learning (ML)-based models of surgery durations across two sites in the US and Singapore. We used the Duke Protected Analytics Computing Environment (PACE) to facilitate data-sharing and big data analytics across the US and Singapore. Data from all colorectal surgery patients between 1 January 2012 and 31 December 2017 in Singapore and, 1 January 2015 to 31 December 2019 in the US were used, and 7585 cases and 3597 single and multiple procedure cases from Singapore and US were included. The ML models were based on categorical gradient boosting (CatBoost) models trained on common data fields shared by both institutions. The procedure codes were based on the Table of Surgical Procedure (TOSP) (Singapore) and the Current Procedural Terminology (CPT) codes (US). The two types of codes were mapped by surgical experts. The CPT codes were then transformed into the relative value unit (RVU). The ML models outperformed the baseline MA models. The MA, scheduled durations and procedure codes were found to have higher loadings as compared to surgeon factors. We further demonstrated the use of the Duke PACE in facilitating data-sharing and big data analytics.

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