Ants work harder during consensus decision-making in small groups

Individuals derive many benefits from being social, one of which is improved accuracy of decision-making, the so-called ‘wisdom of the crowds’ effect. This advantage arises because larger groups can pool information from more individuals. At present, limited empirical data indicate that larger groups outperform smaller ones during consensus decision-making in human and non-human animals. Inaccurate decisions can lead to significant costs, and we might therefore expect individuals in small groups to employ mechanisms to compensate for the lack of numbers. Small groups may be able to maintain decision accuracy if individuals are better informed than those in larger groups and/or by increasing the proportion of the group involved in collective decision-making relative to larger groups. In this study, we use interactive computer vision software to investigate individual contributions to consensus decision-making during house-hunting in different sized groups of the ant Myrmecina nipponica. We show that individuals in small colonies invest greater effort in the consensus decision process than those in large colonies and should be better informed as a result. This may act to ameliorate the limitations of group size, but could leave smaller groups more susceptible to additional stresses.     

Optimal control of distributed hyperbolic systems with deviating arguments

In this paper optimal control problems for hyperbolic systems in which deviating arguments appear in the integral form both in the state equations and in the boundary conditions are considered. Making use of Lion's scheme, necessary and sufficient conditions of optimality for the Neumann problem are derived. A simple example of application is also provided.     

Decision rules extraction from data stream in the presence of changing context for diabetes treatment

The knowledge extraction is an important element of the e-Health system. In this paper, we introduce a new method for decision rules extraction called Graph-based Rules Inducer to support the medical interview in the diabetes treatment. The emphasis is put on the capability of hidden context change tracking. The context is understood as a set of all factors affecting patient condition. In order to follow context changes, a forgetting mechanism with a forgetting factor is implemented in the proposed algorithm. Moreover, to aggregate data, a graph representation is used and a limitation of the search space is proposed to protect from overfitting. We demonstrate the advantages of our approach in comparison with other methods through an empirical study on the Electricity benchmark data set in the classification task. Subsequently, our method is applied in the diabetes treatment as a tool supporting medical interviews.     

To boldly go: an occam-π mission to engineer emergence

Future systems will be too complex to design and implement explicitly. Instead, we will have to learn to engineer complex behaviours indirectly: through the discovery and application of local rules of behaviour, applied to simple process components, from which desired behaviours predictably emerge through dynamic interactions between massive numbers of instances. This paper describes a process-oriented architecture for fine-grained concurrent systems that enables experiments with such indirect engineering. Examples are presented showing the differing complex behaviours that can arise from minor (non-linear) adjustments to low-level parameters, the difficulties in suppressing the emergence of unwanted (bad) behaviour, the unexpected relationships between apparently unrelated physical phenomena (shown up by their separate emergence from the same primordial process swamp) and the ability to explore and engineer completely new physics (such as force fields) by their emergence from low-level process interactions whose mechanisms can only be imagined, but not built, at the current time.     

Investigating fuel-cell transport limitations using hydrogen limiting current

Reducing mass-transport losses in polymer-electrolyte fuel cells (PEFCs) is essential to increase their power density and reduce overall stack cost. At the same time, cost also motivates the reduction in expensive precious-metal catalysts, which results in higher local transport losses in the catalyst layers. In this paper, we use a hydrogen-pump limiting-current setup to explore the gas-phase transport losses through PEFC catalyst layers and various gas-diffusion and microporous layers. It is shown that the effective diffusivity in the gas-diffusion layers is a strong function of liquid saturation. In addition, it is shown how the catalyst layer unexpectedly contributes significantly to the overall measured transport resistance. This is especially true for low catalyst loadings. It is also shown how the various losses can be separated into different mechanisms including diffusional processes and mass-dependent and independent ones, where the data suggests that a large part of the transport resistance in catalyst layers cannot be attributed to a gas-phase diffusional process. The technique is promising for deconvoluting transport losses in PEFCs.     

Numerical Study of a Thermal Energy Storage Device Utilizing Graphite Foam Infiltrated with a Phase Change Material

Phase change materials (PCM) utilized for energy storage have notoriously low thermal conductivities. As a result, systems based only on a PCM have large internal thermal gradients and slow reaction times making them impractical for most applications. To overcome these issues, various approaches have been utilized to increase the conductivity of the PCM systems. One approach includes the utilization of porous, high thermal conductivity graphite foam infiltrated with a PCM. Here, a numerical approach was employed in order to study the graphite foam/PCM thermal energy storage system (TES). The numerical model was constructed to emulate an experimental set-up allowing for comparisons between the two. The numerical simulation results exhibited accurate time-dependent temperatures at various locations as well as a history of the melt-front’s progression when compared to the experimental data. Due to the model’s successful capture of the transient response of the TES, it is feasible to employ the numerical procedure for designing subsequent thermal energy storage systems.     

Effective Number of Samples and Pseudo-Random Nonlinear Distortions in Digital OFDM Coded Signal

This paper concerns the theoretical modeling of OFDM coded signal’s degradation caused by pseudo-random nonlinear distortions introduced by an analog-to-digital or digital-to-analog converter. A new quantity, the effective number of samples, is defined and used for the derivation of accurate expressions for the autocorrelation function and the total power of the distortions. The derivation is based on a probabilistic model of the signal and its transition probability. It is shown that for digital (discrete and quantized) signals the effective number of samples replaces the total number of samples and is the proper quantity defining their properties.     

Design and statistical analysis of plasma coatings on superalloy for gas turbine applications

The Influence of plasma sprayed NiCr–Cr₂O₃ and Al₂O₃–40% TiO₂ (A40T) coatings on Inconel 617 are investigated at 900 and 1000 °C under mixed salt environment for gas turbine applications. The results revealed that the plasma coated alloy exhibits lower mass change than bare alloy in the Na₂SO₄ and NaCl salt mixture with and without V₂O₅. The response to the experimental analysis is statistically evaluated using 2³ factorial design. Further the exposed samples were analyzed by means of optical image, SEM, EDS and XRD results. The EDS and XRD analysis revealed that the oxides are rich in Ni and Mo on the surface of the samples. The coated samples exhibited better corrosion resistance in A40T when compared to NiCr–Cr₂O₃ coatings than bare alloy.