The power of shell rapping influences rates of eviction in hermit crabs

Hermit crabs fight for ownership of shells, and shell exchange may occur after a period of shell rapping, involving the initiating or attacking crab bringing its shell rapidly and repeatedly into contact with the shell of the noninitiator or defender, in a series of bouts. The temporal pattern of rapping contains information about the motivation and/or relative resource holding potential (RHP) of the initiator and acts as a repeated signal of stamina. Here we investigated the role of the force with which the rapping is performed and how this is related to the temporal pattern of rapping by rubberizing the external surface of shells. Initiators that are prevented from rapping with their usual level of force persist with the activity for longer over the whole encounter but use fewer raps per bout and are less likely to effect an exchange than those supplied with control shells. The fact that the force of rapping affects the likelihood of a crab being victorious suggests that either the force of rapping contains information about motivation or RHP or that force directly affects noninitiators, reducing their ability to maintain an adequate grip on their shells. The data suggest that shell rapping is an agonistic signal rather than one that provides information useful to the noninitiator, as has been suggested by the negotiation model of shell exchange.

Short, medium and long term load forecasting model and virtual load forecaster based on radial basis neural networks

Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications. However, they are not typically used in medium and long term load forecasting (MLTLF) electric power models because of the difficulties associated with collecting and processing the necessary data. Virtual instrument (VI) techniques can be applied to electric power load forecasting but this is rarely reported in the literature. In this paper, we investigate the modelling and design of a VI for short, medium and long term load forecasting using ANNs. Three ANN models were built for STLF of electric power. These networks were trained using historical load data and also considering weather data which is known to have a significant affect of the use of electric power (such as wind speed, precipitation, atmospheric pressure, temperature and humidity). In order to do this a V-shape temperature processing model is proposed. With regards MLTLF, a model was developed using radial basis function neural networks (RBFNN). Results indicate that the forecasting model based on the RBFNN has a high accuracy and stability. Finally, a virtual load forecaster which integrates the VI and the RBFNN is presented.

A Tower-Shadow Model for Wind Turbines Using a Wavelet-Prony Method

The increasing penetration of wind generation on the Island of Ireland has been accompanied by close investigation of low-frequency pulsations contained within active power flow. A primary concern is excitation of low-frequency oscillation modes already present on the system, particularly the 0.75 Hz mode as a consequence of interconnection between the Northern and Southern power system networks. In order to determine whether the prevalence of wind generation has a negative effect (excites modes) or positive impact (damping of modes) on the power system, oscillations must be measured and characterised. Using time – frequency methods, this paper presents work that has been conducted to extract features from low-frequency active power pulsations to determine the composition of oscillatory modes which may impact on dynamic stability. The paper proposes a combined wavelet-Prony method to extract modal components and determine damping factors. The method is exemplified using real data obtained from wind farm measurements.

Primitive living chitons: Morphological cladistic analysis as a model for character evaluation

Chitons are often referred to as “living fossils” in part because they are proposed as one of the earliest-diverging groups of living molluscs, but also because the gross morphology of the polyplacophoran shell has been conserved for hundreds of millions of years. As such, the analysis of evolution and radiation within polyplacophorans is of considerable interest not only for resolving the shape of pan-molluscan phylogeny but also as model organisms for the study of character evolution. This study presents a new, rigorous cladistic analysis of the morphological characters used in taxonomic descriptions for chitons in the living suborder Lepidopleurina Thiele, 1910 (the earliest-derived living group of chitons). Shell-based characters alone entirely fail to recover any recognized subdivisions within the group, which may raise serious questions about the application of fossil data (from isolated shell valves). New analysis including characters from girdle armature and gill arrangements recovers some genera within the group but also points to the lack of monophyly within the main genus Leptochiton Gray, 1847. Additional characters from molecular data and soft anatomy, used in combination, are clearly needed to resolve questions of chiton relationships. However, the data sets currently available already provide interesting insights into the analytical power of traditional morphology as well as some knowledge about the early evolution and radiation of this group.

The Effect of Risk Sensitivity on Collaborative New Product Development in a Supply Chain

The increasing risks and costs of new product development require firms to collaborate with their supply chain partners in product management. In this paper, a supply chain model is proposed with one risk-neutral supplier and one risk-averse manufacturer. The manufacturer has an opportunity to enhance demand by developing a new product, but both the actual demand for new product and the supplier’s wholesale price are uncertain. The supplier has an incentive to share risks of new product development via an advance commitment to wholesale price for its own profit maximization. The effects of the manufacturer’s risk sensitivity on the players’ optimal strategies are analyzed and the trade-off between innovation incentives and pricing flexibility is investigated from the perspective of the supplier. The results highlight the significant role of risk sensitivity in collaborative new product development, and it is found that the manufacturer’s innovation level and retail price are always decreasing in the risk sensitivity, and the supplier prefers commitment to wholesale price only when the risk sensitivity is below a certain threshold.

Power Stored and Quality Factors in Frequency Selective Surfaces at THz Frequencies

A study of the external, loaded and unloaded quality factors for frequency selective surfaces (FSSs) is presented. The study is focused on THz frequencies between 5 and 30 THz, where ohmic losses arising from the conductors become important. The influence of material properties, such as metal thickness, conductivity dispersion and surface roughness, is investigated. An equivalent circuit that models the FSS in the presence of ohmic losses is introduced and validated by means of full-wave results. Using both full-wave methods as well as a circuit model, the reactive energy stored in the vicinity of the FSS at resonance upon plane-wave incidence is presented. By studying a doubly periodic array of aluminium strips, it is revealed that the reactive power stored at resonance increases rapidly with increasing periodicity. Moreover, it is demonstrated that arrays with larger periodicity-and therefore less metallisation per unit area-exhibit stronger thermal absorption. Despite this absorption, arrays with higher periodicities produce higher unloaded quality factors. Finally, experimental results of a fabricated prototype operating at 14 THz are presented.

How can cells sense the elasticity of a substrate? An analysis using a cell tensegrity model

A eukaryotic cell attaches and spreads on substrates, whether it is the extracellular matrix naturally produced by the cell itself, or artificial materials, such as tissue-engineered scaffolds. Attachment and spreading require the cell to apply forces in the nN range to the substrate via adhesion sites, and these forces are balanced by the elastic response of the substrate. This mechanical interaction is one determinant of cell morphology and, ultimately, cell phenotype. In this paper we use a finite element model of a cell, with a tensegrity structure to model the cytoskeleton of actin filaments and microtubules, to explore the way cells sense the stiffness of the substrate and thereby adapt to it. To support the computational results, an analytical 1D model is developed for comparison. We find that (i) the tensegrity hypothesis of the cytoskeleton is sufficient to explain the matrix-elasticity sensing, (ii) cell sensitivity is not constant but has a bell-shaped distribution over the physiological matrix-elasticity range, and (iii) the position of the sensitivity peak over the matrix-elasticity range depends on the cytoskeletal structure and in particular on the F-actin organisation. Our model suggests that F-actin reorganisation observed in mesenchymal stem cells (MSCs) in response to change of matrix elasticity is a structural-remodelling process that shifts the sensitivity peak towards the new value of matrix elasticity. This finding discloses a potential regulatory role of scaffold stiffness for cell differentiation.

1/f(alpha) fluctuations in a ricepile model

The temporal fluctuation of the average slope of a ricepile model is investigated. It is found that the power spectrum S(f) scales as 1/f(alpha) with alpha approximate to 1.3 when grains of rice are added only to one end of the pile. If grains are randomly added to the pile, the power spectrum exhibits 1/f(2) behavior. The profile fluctuations of the pile under different driving mechanisms are also discussed.

Determining the parametric effectiveness of a CAD model

The motivation for this paper is to present an approach for rating the quality of the parameters in a computer-aided design model for use as optimization variables. Parametric Effectiveness is computed as the ratio of change in performance achieved by perturbing the parameters in the optimum way, to the change in performance that would be achieved by allowing the boundary of the model to move without the constraint on shape change enforced by the CAD parameterization. The approach is applied in this paper to optimization based on adjoint shape sensitivity analyses. The derivation of parametric effectiveness is presented for optimization both with and without the constraint of constant volume. In both cases, the movement of the boundary is normalized with respect to a small root mean squared movement of the boundary. The approach can be used to select an initial search direction in parameter space, or to select sets of model parameters which have the greatest ability to improve model performance. The approach is applied to a number of example 2D and 3D FEA and CFD problems.

Prediction-Based Power-Performance Adaptation of Multithreaded Scientific Codes

Computing has recently reached an inflection point with the introduction of multicore processors. On-chip thread-level parallelism is doubling approximately every other year. Concurrency lends itself naturally to allowing a program to trade performance for power savings by regulating the number of active cores; however, in several domains, users are unwilling to sacrifice performance to save power. We present a prediction model for identifying energy-efficient operating points of concurrency in well-tuned multithreaded scientific applications and a runtime system that uses live program analysis to optimize applications dynamically. We describe a dynamic phase-aware performance prediction model that combines multivariate regression techniques with runtime analysis of data collected from hardware event counters to locate optimal operating points of concurrency. Using our model, we develop a prediction-driven phase-aware runtime optimization scheme that throttles concurrency so that power consumption can be reduced and performance can be set at the knee of the scalability curve of each program phase. The use of prediction reduces the overhead of searching the optimization space while achieving near-optimal performance and power savings. A thorough evaluation of our approach shows a reduction in power consumption of 10.8 percent, simultaneous with an improvement in performance of 17.9 percent, resulting in energy savings of 26.7 percent.

THE EFFECT OF POWER ULTRASOUND ON THE OXIDATIVE DISSOLUTION OF RUO2.XH2O BY BROMATE IONS

The effects of continuous sonication and presonication on the kinetics of oxidative dissolution of ruthenium dioxide hydrate by bromate ions under acidic conditions are reported. Compared with unsonicated and presonicated dispersions the overall rate of dissolution of continuously sonicated dispersions is significantly greater due to a reduction in the average particle size and, hence, an increase in the specific surface area. Powder dispersions subjected to continuous ultrasound and presonication exhibit an initial induction period in their corrosion kinetics; the length of this induction period increases with increasing presonication. This corrosion feature is retained in the dissolution kinetics of powder samples which have been subjected to pre-ultrasound, but which are then stirred during the dissolution process. It is believed that this apparent permanent change in the nature of the powder particles is due to the ultrasound induced formation of a very thin layer of a largely unreactive form of ruthenium dioxide (possibly due to partial dehydration) on the surface of the powder particles. A kinetic scheme, based on this model, is used to account for the observed kinetics of dissolution of RuO2 . xH2O which have been subjected to both continuous sonication and presonication.