Book review: The Postwar Legacy of Appeasement: British Foreign Policy since 1945. By R. Gerald Hughes (London: Bloomsbury, 2014), pp. xii +331pp, AU$40.99 (pb).

“Appeaser”; since the Second World War there is perhaps no other label that prime ministers and presidents in the English-speaking world have strived so hard to avoid. It is extraordinary how powerful and long-lasting the term appeasement, the name Neville Chamberlain and the place Munich have been in the discourse of post-war international relations. It is a reflection of the all-powerful historical legacy of the Second World War that these terms still resonate with policy makers and their publics well into the twenty-first century. Such a phenomenon deserves scholarly attention and R. Gerald Hughes has done justice to this topic in his very fine book The Postwar Legacy of Appeasement: British Foreign Policy Since 1945.

An Integrated Soft Computing Approach to Hughes Syndrome Risk Assessment

The AntiPhospholipid Syndrome (APS) is an acquired autoimmune disorder induced by high levels of antiphospholipid antibodies that cause arterial and veins thrombosis, as well as pregnancy-related complications and morbidity, as clinical manifestations. This autoimmune hypercoagulable state, usually known as Hughes syndrome, has severe consequences for the patients, being one of the main causes of thrombotic disorders and death. Therefore, it is required to be preventive; being aware of how probable is to have that kind of syndrome. Despite the updated of antiphospholipid syndrome classification, the diagnosis remains difficult to establish. Additional research on clinically relevant antibodies and standardization of their quantification are required in order to improve the antiphospholipid syndrome risk assessment. Thus, this work will focus on the development of a diagnosis decision support system in terms of a formal agenda built on a Logic Programming approach to knowledge representation and reasoning, complemented with a computational framework based on Artificial Neural Networks. The proposed model allows for improving the diagnosis, classifying properly the patients that really presented this pathology (sensitivity higher than 85%), as well as classifying the absence of APS (specificity close to 95%).