Age-Related Tooth Wear Differs between Forest and Savanna Primates

Tooth wear in primates is caused by aging and ecological factors. However, comparative data that would allow us to delineate the contribution of each of these factors are lacking. Here, we contrast age-dependent molar tooth wear by scoring percent of dentine exposure (PDE) in two wild African primate populations from Gabonese forest and Kenyan savanna habitats. We found that forest-dwelling mandrills exhibited significantly higher PDE with age than savanna yellow baboons. Mandrills mainly feed on large tough food items, such as hard-shell fruits, and inhabit an ecosystem with a high presence of mineral quartz. By contrast, baboons consume large amounts of exogenous grit that adheres to underground storage organs but the proportion of quartz in the soils where baboons live is low. Our results support the hypothesis that not only age but also physical food properties and soil composition, particularly quartz richness, are factors that significantly impact tooth wear. We further propose that the accelerated dental wear in mandrills resulting in flatter molars with old age may represent an adaptation to process hard food items present in their environment.

Classifier combination for in vivo magnetic resonance spectra of brain tumours

Peer-reviewed

Classifier combination for in vivo magnetic resonance spectra of brain tumours

In this paper we present a multi-stage classifier for magnetic resonance spectra of human brain tumours which is being developed as part of a decision support system for radiologists. The basic idea is to decompose a complex classification scheme into a sequence of classifiers, each specialising in different classes of tumours and trying to reproducepart of the WHO classification hierarchy. Each stage uses a particular set of classification features, which are selected using a combination of classical statistical analysis, splitting performance and previous knowledge.Classifiers with different behaviour are combined using a simple voting scheme in order to extract different error patterns: LDA, decision trees and the k-NN classifier. A special label named "unknown¿ is used when the outcomes of the different classifiers disagree. Cascading is alsoused to incorporate class distances computed using LDA into decision trees. Both cascading and voting are effective tools to improve classification accuracy. Experiments also show that it is possible to extract useful information from the classification process itself in order to helpusers (clinicians and radiologists) to make more accurate predictions and reduce the number of possible classification mistakes.

A Sparse Bayesian Approach for Joint Feature Selection and Classifier Learning

Peer-reviewed

Climate Change and the Cost of Carbon Sequestration: The Case of Forest Management

The Kyoto protocol allows Annex I countries to deduct carbon sequestered by land use, land-use change and forestry from their national carbon emissions. Thornley and Cannell (2000) demonstrated that the objectives of maximizing timber and carbon sequestration are not complementary. Based on this finding, this paper determines the optimal selective management regime taking into account the underlying biophysical and economic processes. The results show that the net benefits of carbon storage only compensate the decrease in net benefits of timber production once the carbon price has exceeded a certain threshold value. The sequestration costs are significantly lower than previous estimates

A survey on coverage path planning for robotics

Coverage Path Planning (CPP) is the task of determining a path that passes over all points of an area or volume of interest while avoiding obstacles. This task is integral to many robotic applications, such as vacuum cleaning robots, painter robots, autonomous underwater vehicles creating image mosaics, demining robots, lawn mowers, automated harvesters, window cleaners and inspection of complex structures, just to name a few. A considerable body of research has addressed the CPP problem. However, no updated surveys on CPP reflecting recent advances in the field have been presented in the past ten years. In this paper, we present a review of the most successful CPP methods, focusing on the achievements made in the past decade. Furthermore, we discuss reported field applications of the described CPP methods. This work aims to become a starting point for researchers who are initiating their endeavors in CPP. Likewise, this work aims to present a comprehensive review of the recent breakthroughs in the field, providing links to the most interesting and successful works

Statistical Optimum Design of Heat Exchangers

The optimal design of a heat exchanger system is based on given model parameters together with given standard ranges for machine design variables. The goals set for minimizing the Life Cycle Cost (LCC) function which represents the price of the saved energy, for maximizing the momentary heat recovery output with given constraints satisfied and taking into account the uncertainty in the models were successfully done. Nondominated Sorting Genetic Algorithm II (NSGA-II) for the design optimization of a system is presented and implemented inMatlab environment. Markov ChainMonte Carlo (MCMC) methods are also used to take into account the uncertainty in themodels. Results show that the price of saved energy can be optimized. A wet heat exchanger is found to be more efficient and beneficial than a dry heat exchanger even though its construction is expensive (160 EUR/m2) compared to the construction of a dry heat exchanger (50 EUR/m2). It has been found that the longer lifetime weights higher CAPEX and lower OPEX and vice versa, and the effect of the uncertainty in the models has been identified in a simplified case of minimizing the area of a dry heat exchanger.