To investigate the determinants of acute mountain sickness (AMS) and of summiting in expedition-style mountaineering, 919 mountaineers (15.4% female) leaving Aconcagua Provincial Park at the end of an expedition to Mt. Aconcagua (6962 m) via the normal route were retrospectively evaluated by questionnaires. Symptoms of AMS were reported from the day when mountaineers felt worst. The prevalence of AMS, defined as a Lake Louise Score (self-assessment) > 4, was 39%. Low AMS scores were associated with faster ascent rates. The following parameters were independent predictors for AMS: no susceptibility for AMS (odds ratio, OR, 0.24; 95% confidence interval 0.17 to 0.35) more than 10 exposures per year above 3000 m (OR 0.60; 0.41 to 0.86), and previous exposures above 6000 m (OR, 0.48; 0.33 to 0.68). This last variable increased the OR for summiting 3.7-fold while female gender reduced this OR to 0.41 (0.25 to 0.67). Susceptibility and few exposures to high altitude are major predictors for AMS on Aconcagua, but AMS does not substantially reduce the chances for summiting. Those who are often in the mountains and who have already climbed to altitudes above 6000 m and are not susceptible for AMS have the best options for summiting Aconcagua.

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The integration of machine learning techniques and metaheuristic algorithms is an area of interest due to the great potential for applications. In particular, using these hybrid techniques to solve combinatorial optimization problems (COPs) to improve the quality of the solutions and convergence times is of great interest in operations research. In this article, the db-scan unsupervised learning technique is explored with the goal of using it in the binarization process of continuous swarm intelligence metaheuristic algorithms. The contribution of the db-scan operator to the binarization process is analyzed systematically through the design of random operators. Additionally, the behavior of this algorithm is studied and compared with other binarization methods based on clusters and transfer functions (TFs). To verify the results, the well-known set covering problem is addressed, and a real-world problem is solved. The results show that the integration of the db-scan technique produces consistently better results in terms of computation time and quality of the solutions when compared with TFs and random operators. Furthermore, when it is compared with other clustering techniques, we see that it achieves significantly improved convergence times.

A B S T R A C T Although metal foams are a relatively new material, substantial knowledge has been accumulated about their mechanical properties and behaviour under monotonic loads and tension-tension and compression-compression cyclic loads. However, there are very few reports of the behaviour of metal foams under tension-compression-reversed loading. In this paper, we examine some of the rare published data regarding the tension-compression cyclic response of metal foams, develop a statistical model of the fatigue lifetime and propose two damage accumulation models for aluminium-closed cell foams subjected to a fully reversed cyclic loading. In developing these models a fatigue analysis and a failure criterion for the material are needed; the fatigue models considered are the Coffin-Manson and the statistical Weibull model, and the failure criterion used is the one described by Ingraham et al. (Ingraham, M.D., DeMaria, C.J., Issen, K.A. and Morrison, D.J.L. (2009). Mater. Sci. Eng. A. 504:150-156). The models developed are compared with the experimental published data by Ingraham et al. (Ingraham, M.D., DeMaria, C.J., Issen, K.A. and Morrison, D.J.L. (2009). Mater. Sci. Eng. A. 504:150-156) and a final analysis was performed to determine whether it is preferable to use the total or plastic strain amplitude for the fatigue analysis. A, A 1 , A 2 = Damage accumulation model parameters B = Threshold value of lifetime C = Threshold value of strain amplitude c = Fatigue ductility exponent D = Damage accumulation model H C = Compressive pre-peak slope H T = Tensile pre-peak slope h = Function that provide the damage accumulation model N = Number of cycles N * = Number of cycles at failure level N f = Number of cycles to failure p = Probability of failure R = Damage level R 0 = Initial damage level R * = Damage level at failure v i = Variables of the damage accumulation model β = Weibull shape parameter