Mining Markov Network Surrogates for Value-Added Optimisation

Surrogate fitness functions are a popular technique for speeding up metaheuristics, replacing calls to a costly fitness function with calls to a cheap model. However, surrogates also represent an explicit model of the fitness function, which can be exploited beyond approximating the fitness of solutions. This paper proposes that mining surrogate fitness models can yield useful additional information on the problem to the decision maker, adding value to the optimisation process. An existing fitness model based on Markov networks is presented and applied to the optimisation of glazing on a building facade. Analysis of the model reveals how its parameters point towards the global optima of the problem after only part of the optimisation run, and reveals useful properties like the relative sensitivities of the problem variables

Experiences Marketing: A Cultural Philosophy for Contemporary Hospitality Marketing Studies

This article explores the landscape of contemporary hospitality marketing. It is argued that the teaching and academic discussions that surround the subject area adopt a predominantly positivistic approach; although important, that does not adequately reflect the nature of the industry or the products offered. Such a metrics-oriented position, although significant in the formulation of marketing strategy, does not reflect the complex experiential, nontangible nature of the hospitality product. This article presents a culturally located philosophy that reflects the multifaceted nature of the industry. The philosophy is underpinned by three precepts that draw from a multidisciplinary theoretical framework to create a more subject-specific approach to marketing, that when woven with traditional approaches can create a more effective and informed contemporary approach

Post-disaster tourism: building resilience through community-led approaches in the aftermath of the 2011 disasters in Japan

Post-disaster tourism is often perceived as a form of Dark Tourism associated with death, loss and destruction. In Japan, the term Dark Tourism has gained prominence following the 2011 Great East Japan Earthquake and Tsunami. This paper focuses on a community-led approach to post-disaster tourism development, initiated in the coastal area of Minamisanriku and labelled by the locals Blue Tourism. From its inception Blue Tourism incorporated non-dark activities which concentrated on the beauty of nature, social and environmental sustainability and the development of an enriched tourist experience. Its co-creational ethos helped transform some of the negative narratives of loss associated with Dark Tourism into positive accounts of communal renewal and hope. The paper highlights the limitations of Dark Tourism to post-disaster recovery and contributes new insights to the community-based tourism literature. We argue that Blue Tourism is not a type of Dark Tourism but a form of resilience which builds around local place-based practices and traditional community knowledge. Consequently, it is capable of achieving sustainable disaster recovery and tourist satisfaction simultaneously

Making sense of urban food festivals: cultural regeneration, disorder and hospitable cities

This article examines urban food festivals, and in doing so it carries out a case study of Nottingham’s Food and Drink Festival (NFDF). It contends that such festivals need to be understood in relation to local contexts, such as the reputation for alcohol-related disorder associated with Nottingham’s night-time economy. Rather than being used to attract tourism, NFDF was primarily directed at existing residents of Nottingham, where it sought to produce particular kinds of guests who would be able to invest in the city’s wider regeneration. Here, the article draws on recent academic work on hospitality in demonstrating how NFDF attempted to rebrand the city centre as a more hospitable place. It concludes by showing how visitors to NFDF exhibited a sense of generosity and pride, and argues that the meaning of urban food festivals cannot therefore simply be reduced to the logic of neoliberal governance

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability