Surrogate Assisted Optimisation for Travelling Thief Problems

The travelling thief problem (TTP) is a multi-component optimisation problem involving two interdependent NP-hard components: the travelling salesman problem (TSP) and the knapsack problem (KP). Recent state-of-the-art TTP solvers modify the underlying TSP and KP solutions in an iterative and interleaved fashion. The TSP solution (cyclic tour) is typically changed in a deterministic way, while changes to the KP solution typically involve a random search, effectively resulting in a quasi-meandering exploration of the TTP solution space. Once a plateau is reached, the iterative search of the TTP solution space is restarted by using a new initial TSP tour. We propose to make the search more efficient through an adaptive surrogate model (based on a customised form of Support Vector Regression) that learns the characteristics of initial TSP tours that lead to good TTP solutions. The model is used to filter out non-promising initial TSP tours, in effect reducing the amount of time spent to find a good TTP solution. Experiments on a broad range of benchmark TTP instances indicate that the proposed approach filters out a considerable number of non-promising initial tours, at the cost of omitting only a small number of the best TTP solutions

The Remarkable Beneficial Effect of Adding Oral Simvastatin to Topical Betamethasone for Treatment of Psoriasis: A Double-blind, Randomized, Placebo-controlled Study

Psoriasis is a common chronic inflammatory disease with unpredictableprognosis. Given the immunomodulatory effects of statins, the present study was conducted to determine whether the addition of orally administered simvastatin to the topical betamethasone, a standard antipsoriatic treatment, can produce a more powerful therapeutic response against this clinical conundrum.In a double-blind study, 30 patients with plaque type psoriasis were randomly divided into two equal treatment groups. Group 1 received oralsimvastatin (40 mg/d) plus topical steroid (50% betamethasone in petrolatum) for 8 weeks and group 2 received oral placebo plus the same topical steroid for the same time period. Psoriasis Area and Severity Index (PASI) score was checked before and at the end of the treatment period.PASI score decreased significantly in both groups, but the decline of PASI score was more significant in patients who received simvastatin(Mann-Whitney test; P-value=0.001). No side effect or any laboratory abnormality was detected in patients.Our work, which is the first doubleblind, randomized, placebo-controlled study on this subject, shows that oral simvastatin enhances the therapeutic effect of topical steroids against psoriasis. The increased risk of cardiovascular accidents in psoriatic patients and the protective effect of statins against cardiovascular disease further encourages their use in the treatment of this clinical conundrum.Keywords: Simvastatin – Psoriasis – Treatment – Topical Steroid

Mental Health Changes and Its Predictors in Adolescents using the Path Analytic Model: A 7-Year Observational Study.

OBJECTIVE: This 7-year observational study examines the hours of TV-watching, phone conversation with friends, using the internet, and physical activity as predictors of mental health among adolescents in south of Iran. METHODS: At the baseline (in 2005), the participants were 2584 high school students in the 9th to 11th grade. At the baseline, 30% of the available participants (n = 775) were selected in the follow-up (2012) using convenience sampling method. This study used the path analysis to examine the predictors of mental health and to obtain direct, indirect and total effects of the independent variables. RESULTS: At the baseline (2005), female gender, internet use, maternal education, physical activity and father's education were associated with mental health (p<0.05). Baseline mental health, internet use and physical activity predicted mental health of the participants in the follow up (p<0.05). CONCLUSION: The findings of the study revealed that better mental health in later life is associated with better mental health at baseline, male gender, higher physical activity and phone communication with friends, and less use of the internet and TV

Solving Travelling Thief Problems using Coordination Based Methods

A travelling thief problem (TTP) is a proxy to real-life problems such as postal collection. TTP comprises an entanglement of a travelling salesman problem (TSP) and a knapsack problem (KP) since items of KP are scattered over cities of TSP, and a thief has to visit cities to collect items. In TTP, city selection and item selection decisions need close coordination since the thief's travelling speed depends on the knapsack's weight and the order of visiting cities affects the order of item collection. Existing TTP solvers deal with city selection and item selection separately, keeping decisions for one type unchanged while dealing with the other type. This separation essentially means very poor coordination between two types of decision. In this paper, we first show that a simple local search based coordination approach does not work in TTP. Then, to address the aforementioned problems, we propose a human designed coordination heuristic that makes changes to collection plans during exploration of cyclic tours. We further propose another human designed coordination heuristic that explicitly exploits the cyclic tours in item selections during collection plan exploration. Lastly, we propose a machine learning based coordination heuristic that captures characteristics of the two human designed coordination heuristics. Our proposed coordination based approaches help our TTP solver significantly outperform existing state-of-the-art TTP solvers on a set of benchmark problems. Our solver is named Cooperation Coordination (CoCo) and its source code is available from https://github.com/majid75/CoCoComment: expanded and revised version of arXiv:1911.0312

Applying a novel combination of techniques to develop a predictive model for diabetes complications

© 2015 Sangi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Among the many related issues of diabetes management, its complications constitute the main part of the heavy burden of this disease. The aim of this paper is to develop a risk advisor model to predict the chances of diabetes complications according to the changes in risk factors. As the starting point, an inclusive list of (k) diabetes complications and (n) their correlated predisposing factors are derived from the existing endocrinology text books. A type of data meta-analysis has been done to extract and combine the numeric value of the relationships between these two. The whole n (risk factors) - k (complications) model was broken down into k different (n-1) relationships and these (n-1) dependencies were broken into n (1-1) models. Applying regression analysis (seven patterns) and artificial neural networks (ANN), we created models to show the (1-1) correspondence between factors and complications. Then all 1-1 models related to an individual complication were integrated using the naïve Bayes theorem. Finally, a Bayesian belief network was developed to show the influence of all risk factors and complications on each other. We assessed the predictive power of the 1-1 models by R2, F-ratio and adjusted R2 equations; sensitivity, specificity and positive predictive value were calculated to evaluate the final model using real patient data. The results suggest that the best fitted regression models outperform the predictive ability of an ANN model, as well as six other regression patterns for all 1-1 models