Abstract
One major practical problem when applying traditional metaheuristics seems to be their strong dependency on parameter tuning. This issue is frequently pointed out as a major shortcoming of metaheuristics and is often a reason for Decision-Makers to reject using this type of approach in practical situations. In this paper we present a new search strategy - Constraint Oriented Neighbourhoods - that tries to overcome the referred drawback. The aim is to control the grade of randomness of metaheuristics, by defining "special" neighbourhood movements, that lead to a more robust heuristic, less dependent on parameter tuning. This is achieved by selecting and applying particular movements that take into account the potential violation of problem constraints. The strategy is illustrated in a real problem arising in the area of Power Systems Management - the Unit Commitment Problem, the computational experiments on a set of problem instances systematically outperforming those presented in the literature, both in terms of efficiency, quality of the solution and robustness of the algorithm.

Abstract
This paper describes an approach towards workflow management based on the combination of learning and planning. Assuming that processes cannot be fully described at build-time, the approach makes use of learning techniques, namely Inductive Logic Programming (ILP), in order to discover workflow activities as planning operators. These operators will be subsequently fed to a partial-order planner in order to find the process model as a planning solution. The continuous interplay between learning, planning and execution aims at arriving at a feasible plan by successive refinement of the operators. The approach is illustrated in two simple scenarios. The paper concludes by relating the proposed approach with previous developments in this area.

Abstract
This work represents one of the first successful applications of Autonomous Underwater Vehicles (AUVs) for interdisciplinary coastal research. A monitoring mission to study the shape and estimate the initial dilution of the S. Jacinto sewage outfall plume using an AUV was performed on July 2002. An efficient sampling strategy enabling greater improvements in spatial and temporal range of detection demonstrated that the sewage effluent plume can be clearly traced using naturally occurring tracers in the wastewater. The outfall plume was found at the surface highly influenced by the weak stratification and low currents. Dilution varying with distance downstream was estimated from the plume rise over the outfall diffuser until a nearly constant value of 130:1, 60 m from the diffuser, indicating the near field end. Our results demonstrate that AUVs can provide high-quality measurements of physical properties of effluent plumes in a very effective manner and valuable considerations about the initial mixing processes under real oceanic conditions can be further investigated.

Abstract
Metaheuristics are a class of approximate methods designed to solve hard combinatorial optimization problems arising within various different areas. The importance of metaheuristics results from their ability to continue the search beyond a local optimum so that near-optimal or optimal solutions are efficiently found. In order to solve the backhauling problem associated with mixed and simultaneous delivery and pick-ups, this paper presents a hybrid algorithm which is comprised of the two metaheuristics of tabu search and variable neighbourhood descent. The primary challenge associated with backhauling consists of creating routes in which vehicles are not only required to deliver goods, but also to perform pick-ups at customer locations. The problems associated with these two categories of problems, however, have received little attention in the literature to date. A set of examples taken from the literature with Euclidean cost matrices are presented. Finally, some numerical results are illustrated to show the effectiveness of the proposed approach.

Abstract
Genetic interventions that accelerate or retard aging in mice are crucial in advancing our knowledge over mammalian aging. Yet determining if a given intervention affects the aging process is not straightforward since, for instance, many disease-causing mutations may decrease life span without affecting aging. In this work, we employed the Gompertz model to determine whether several published interventions previously claimed to affect aging in mice do indeed alter the aging process. First, we constructed age-specific mortality tables for a number of mouse cohorts used in longevity experiments and calculated the rate at which mortality increases with age. Estimates of age-independent mortality were also calculated. We found no statistical evidence that GHRHR, IGF1R, INSR, PROP1, or TRX delay or that ATM + TERC, BubR1, klotho, LMNA, PRDX1, p53, WRN + TERC, or TOP3B accelerate mouse aging. Often, changes in the expression of these genes affected age-independent mortality and so they may prove useful to other aspects of medicine. We found statistical evidence that C/EBP, MSRA, SHC1, growth hormone, GHR, PIT1, and PolgA may influence aging in mice. These results were interpreted together with age-related physiological and pathological changes and provide novel insights regarding the role of several genes in the mammalian aging process.

Abstract