Weighting waiting in collective decision-making

Animals searching for food, mates, or a home often need to decide when to stop looking and choose the best option found so far. By re-analyzing experimental data from experiments by Mallon et al. (Behav Ecol Sociobiol 50:352–359, 2001), we demonstrate that house-hunting ant colonies are gradually more committed to new nests during the emigration. Early in house-hunting, individual ants were flexibly committed to new nest sites. However, when carrying to a new nest had started, ants hardly ever switched preference. Using a theoretical model based on experimental data, we test at which stage flexible commitment influences speed and accuracy most. We demonstrate that ant colonies have found a good compromise between impatience and procrastination. Early flexibility combined with later rigidity is identically effective as other strategies that include flexible commitment, but it is particularly good when emigration conditions are harsh.     

Improvement in collective performance with experience in ants

We show that entire ant colonies can improve their collective performance progressively when they repeat the same process. Colonies of Leptothorax albipennis can reduce their total emigration times over successive emigrations. We show that this improvement is based on experience and some memory-like process, rather than a coincidental developmental change or an increased general level of arousal. We demonstrate that the benefits of experience can be lost (i.e. forgotten) if the interval between successive emigrations is too long. We also show that the benefits of experience are more likely to be retained over a longer period if the collective performance has been repeated several times. This is a new demonstration of a process akin to learning in ants and we briefly discuss how it may involve not only improvements in individual performance but also improvements in the ways in which the ants interact with one another.Communicated by L. Sundström     

Application of sludge-based carbonaceous materials in a hybrid water treatment process based on adsorption and catalytic wet air oxidation

This paper describes a preliminary evaluation of the performance of carbonaceous materials prepared from sewage sludges (SBCMs) in a hybrid water treatment process based on adsorption and catalytic wet air oxidation; phenol was used as the model pollutant. Three different sewage sludges were treated by either carbonisation or steam activation, and the physico-chemical properties of the resultant carbonaceous materials (e.g. hardness, BET surface area, ash and elemental content, surface chemistry) were evaluated and compared with a commercial reference activated carbon (PICA F22). The adsorption capacity for phenol of the SBCMs was greater than suggested by their BET surface area, but less than F22; a steam activated, dewatered raw sludge (SA_DRAW) had the greatest adsorption capacity of the SBCMs in the investigated range of concentrations (<0.05 mol L^?1). In batch oxidation tests, the SBCMs demonstrated catalytic behaviour arising from their substrate adsorptivity and metal content. Recycling of SA_DRAW in successive oxidations led to significant structural attrition and a hardened SA_DRAW was evaluated, but found to be unsatisfactory during the oxidation step. In a combined adsorption–oxidation sequence, both the PICA carbon and a selected SBCM showed deterioration in phenol adsorption after oxidative regeneration, but a steady state performance was reached after 2 or 3 cycles.     

Evaluation of pay-for-release conservation incentives for unintentionally caught threatened species

In the developing world, the exploitation of threatened species jeopardizes their permanence in the wild. Because not all captures are intentional, for instance when capture methods have low selectivity, pressure on these species may be lessened by releasing living incidentally caught animals. However, it is often unrealistic to expect people to voluntarily do so because it means foregoing the benefits of resource extraction. Financial incentives for such animal release may foster conservation objectives. Reducing human–animal conflicts, protecting natural habitat, and conserving nests of threatened species are examples of conservation benefits that can be built on financial reward systems. However, incentives aiming to protect unintentionally captured threatened species are scarce. We considered pay for release, a type of ecosystem-service payment designed to foster the release of incidentally captured threatened species. We aimed to determine the best conditions to implement this scheme, its potential benefits (e.g., incentivizing the release of threatened species), and pitfalls and priority research needs (e.g., required conditions for pay for release to work) to show that its global applicability is possible. Given that approaches solely based on education and law enforcement may be ineffective under some circumstances, we argue that pay for release can protect incidentally captured endangered species if used under conditions conducive for its success. When local participants’ intrinsic motivation for conservation is weak, but the release of incidentally live-caught animals into their habitats is readily achievable, pay-for-release schemes could jump start urgently needed conservation efforts against indiscriminate animal harvesting.     

The transition of a line plume to round plume

Environmental Fluid Mechanics - Buoyant turbulent plumes are often categorized by their geometry and described as either round plumes, issuing from a point source, or line/planar plumes, issuing...     

Further validation of artificial neural network-based emissions simulation models for conventional and hybrid electric vehicles

With the advent of hybrid electric vehicles, computer-based vehicle simulation becomes more useful to the engineer and designer trying to optimize the complex combination of control strategy, power plant, drive train, vehicle, and driving conditions. With the desire to incorporate emissions as a design criterion, researchers at West Virginia University have developed artificial neural network (ANN) models for predicting emissions from heavy-duty vehicles. The ANN models were trained on engine and exhaust emissions data collected from transient dynamometer tests of heavy-duty diesel engines then used to predict emissions based on engine speed and torque data from simulated operation of a tractor truck and hybrid electric bus. Simulated vehicle operation was performed with the ADVISOR software package. Predicted emissions (carbon dioxide [CO2] and oxides of nitrogen [NO(x)]) were then compared with actual emissions data collected from chassis dynamometer tests of similar vehicles. This paper expands on previous research to include different driving cycles for the hybrid electric bus and varying weights of the conventional truck. Results showed that different hybrid control strategies had a significant effect on engine behavior (and, thus, emissions) and may affect emissions during different driving cycles. The ANN models underpredicted emissions of CO2 and NO(x) in the case of a class-8 truck but were more accurate as the truck weight increased.