Supply Chain Management in the Hospitality Industry: A research agenda

Supply Chain Management is at the heart of competitive advantage for any organisation. Without Supply Chains, the Hospitality Industry would quickly grind to a halt. There would be no fruit or vegetables in our restaurants, no beer or wine in our bars and no beds or toilets in our hotels. There would be no recycling of glass or the disposal of food products. There would be no customers. Given the importance of Supply Chains to the Hospitality Industry it is perhaps surprising that so little is published about Supply Chains and how Supply Chains can be managed. The aim of this working paper is to define SCM and establish an agenda for undertaking research into this important but neglected topic

Equity weighting and the marginal damage costs of climate change

Climate change would impact different countries differently, and different countries have different levels of development. Equity-weighted estimates of the (marginal) impact of greenhouse gas emissions reflect these differences. Equity-weighted estimates of the marginal damage cost of carbon dioxide emissions are substantially higher than estimates without equity-weights; equity-weights may also change the sign of the social cost estimates. Equity weights need to be normalised. Our estimates differ by two orders of magnitude depending on the region of normalisation. A discounting error of equity weighted social cost of carbon estimates in earlier work (Tol, Energy Journal, 1999), led to an error of a factor two. Equity-weighted estimates are sensitive to the resolution of the impact estimates. Depending on the assumed intra-regional income distribution, estimates may be more than twice as high if national rather than regional impacts are aggregated. The assumed scenario is important too, not only because different scenarios have different emissions and hence warming, but also because different scenarios have different income differences, different growth rates, and different vulnerabilities. Because of this, variations in the assumed inequity aversion have little effect on the marginal damage cost in some scenarios, and a large effect in other scenarios.marginal damage costs, climate change, equity

Nicotine treatment decreases food intake and body weight via a leptin-independent pathway in Psammomys obesus

It has been reported previously that leptin may be involved in nicotine\u27s ability to reduce body weight. Our aim was to investigate whether the anorexic action of nicotine is related to the actions of leptin by utilizing lean leptin-sensitive and obese leptin-resistant Psammomys obesus. Lean and obese P. obesus were assigned to receive nicotine sulphate at 6, 9 or 12 mg/day or saline (control) for 9 days (n = 6-10 in each group), administered using mini-osmotic pumps. Food intake, body weight, plasma leptin concentrations, plasma insulin and blood glucose were measured at baseline and throughout the study period. Nicotine treatment reduced food intake by up to 40% in lean and obese P. obesus. Plasma leptin levels fell significantly only in lean nicotine-treated animals, whereas no changes were observed in obese nicotine-treated animals. However, both lean and obese nicotine-treated animals had similar reductions in body weight. Our results show that nicotine has dramatic effects on food intake and body weight, however, these changes appear to be independent of the leptin signalling pathway.<br /

Equity Weighting and the Marginal Damage Costs of Climate Change

Climate change would impact different countries differently, and different countries have different levels of development. Equity-weighted estimates of the (marginal) impact of greenhouse gas emissions reflect these differences. Equity-weighted estimates of the marginal damage cost of carbon dioxide emissions are substantially higher than estimates without equity-weights; equity-weights may also change the sign of the social cost estimates. Equity weights need to be normalised. Our estimates differ by two orders of magnitude depending on the region of normalisation. A discounting error of equity weighted social cost of carbon estimates in earlier work (Tol, Energy Journal, 1999), led to an error of a factor two. Equity-weighted estimates are sensitive to the resolution of the impact estimates. Depending on the assumed intra-regional income distribution, estimates may be more than twice as high if national rather than regional impacts are aggregated. The assumed scenario is important too, not only because different scenarios have different emissions and hence warming, but also because different scenarios have different income differences, different growth rates, and different vulnerabilities. Because of this, variations in the assumed inequity aversion have little effect on the marginal damage cost in some scenarios, and a large effect in other scenarios.Marginal Damage Costs, Climate Change, Equity

The star cluster mass--galactocentric radius relation: Implications for cluster formation

Whether or not the initial star cluster mass function is established through a universal, galactocentric-distance-independent stochastic process, on the scales of individual galaxies, remains an unsolved problem. This debate has recently gained new impetus through the publication of a study that concluded that the maximum cluster mass in a given population is not solely determined by size-of-sample effects. Here, we revisit the evidence in favor and against stochastic cluster formation by examining the young ($\lesssim$ a few $\times 10^8$ yr-old) star cluster mass--galactocentric radius relation in M33, M51, M83, and the Large Magellanic Cloud. To eliminate size-of-sample effects, we first adopt radial bin sizes containing constant numbers of clusters, which we use to quantify the radial distribution of the first- to fifth-ranked most massive clusters using ordinary least-squares fitting. We supplement this analysis with an application of quantile regression, a binless approach to rank-based regression taking an absolute-value-distance penalty. Both methods yield, within the $1\sigma$ to $3\sigma$ uncertainties, near-zero slopes in the diagnostic plane, largely irrespective of the maximum age or minimum mass imposed on our sample selection, or of the radial bin size adopted. We conclude that, at least in our four well-studied sample galaxies, star cluster formation does not necessarily require an environment-dependent cluster formation scenario, which thus supports the notion of stochastic star cluster formation as the dominant star cluster-formation process within a given galaxy.Comment: ApJ, in press, 39 pages in AAS preprint format, 10 multi-panel figures (some reduced in size to match arXiv compilation routines

Support vector classification analysis of resting state functional connectivity fMRI

Since its discovery in 1995 resting state functional connectivity derived from functional MRI data has become a popular neuroimaging method for study psychiatric disorders. Current methods for analyzing resting state functional connectivity in disease involve thousands of univariate tests, and the specification of regions of interests to employ in the analysis. There are several drawbacks to these methods. First the mass univariate tests employed are insensitive to the information present in distributed networks of functional connectivity. Second, the null hypothesis testing employed to select functional connectivity dierences between groups does not evaluate the predictive power of identified functional connectivities. Third, the specification of regions of interests is confounded by experimentor bias in terms of which regions should be modeled and experimental error in terms of the size and location of these regions of interests. The objective of this dissertation is to improve the methods for functional connectivity analysis using multivariate predictive modeling, feature selection, and whole brain parcellation. A method of applying Support vector classification (SVC) to resting state functional connectivity data was developed in the context of a neuroimaging study of depression. The interpretability of the obtained classifier was optimized using feature selection techniques that incorporate reliability information. The problem of selecting regions of interests for whole brain functional connectivity analysis was addressed by clustering whole brain functional connectivity data to parcellate the brain into contiguous functionally homogenous regions. This newly developed famework was applied to derive a classifier capable of correctly seperating the functional connectivity patterns of patients with depression from those of healthy controls 90% of the time. The features most relevant to the obtain classifier match those previously identified in previous studies, but also include several regions not previously implicated in the functional networks underlying depression.Ph.D.Committee Chair: Hu, Xiaoping; Committee Co-Chair: Vachtsevanos, George; Committee Member: Butera, Robert; Committee Member: Gurbaxani, Brian; Committee Member: Mayberg, Helen; Committee Member: Yezzi, Anthon