Maximizing the Localized Relaxation: The Origin of the Outstanding Oxygen Storage Capacity of kappa-Ce2Zr2O8

Pretty vacant: The excellent oxygen storage capacity (OSC) of ?-Ce2Zr2O8 (see picture; Ce gray, Zr green, O red) is shown to be a result of its unique structural features; after removing oxygen atoms, the structural relaxation is local (vacancy shown in brown), and both the localized structural relaxation and the number of localized structural relaxations are maximized.

DFIG Machine Design for Maximizing Power Output Based on Surrogate Optimization Algorithm

This paper presents a surrogate-model based optimization of a doubly-fed induction generator (DFIG) machine winding design for maximizing power yield. Based on site-specific wind profile data and the machine’s previous operational performance, the DFIG’s stator and rotor windings are optimized to match the maximum efficiency with operating conditions for rewinding purposes. The particle swarm optimization (PSO)-based surrogate optimization techniques are used in conjunction with the finite element method (FEM) to optimize the machine design utilizing the limited available information for the site-specific wind profile and generator operating conditions. A response surface method in the surrogate model is developed to formulate the design objectives and constraints. Besides, the machine tests and efficiency calculations follow IEEE standard 112-B. Numerical and experimental results validate the effectiveness of the proposed technologies.

Independent particle descriptions of tunneling using the many-body quantum transport approach

Currents across thin insulators are commonly taken as single electrons moving across classically forbidden regions; this independent particle picture is well-known to describe most tunneling phenomena. Examining quantum transport from a different perspective, i.e., by explicit treatment of electron-electron interactions, we evaluate different single particle approximations with specific application to tunneling in metal-molecule-metal junctions. We find maximizing the overlap of a Slater determinant composed of single-particle states to the many-body current-carrying state is more important than energy minimization for defining single-particle approximations in a system with open boundary conditions. Thus the most suitable single particle effective potential is not one commonly in use by electronic structure methods, such as the Hartree-Fock or Kohn-Sham approximations.

MISEP method for postnonlinear blind source separation

In this letter, a standard postnonlinear blind source separation algorithm is proposed, based on the MISEP method, which is widely used in linear and nonlinear independent component analysis. To best suit a wide class of postnonlinear mixtures, we adapt the MISEP method to incorporate a priori information of the mixtures. In particular, a group of three-layered perceptrons and a linear network are used as the unmixing system to separate sources in the postnonlinear mixtures, and another group of three-layered perceptron is used as the auxiliary network. The learning algorithm for the unmixing system is then obtained by maximizing the output entropy of the auxiliary network. The proposed method is applied to postnonlinear blind source separation of both simulation signals and real speech signals, and the experimental results demonstrate its effectiveness and efficiency in comparison with existing methods.

The less amorous Gammarus: predation risk affects mating decisions in Gammarus duebeni (Amphipoda)

We examined the trade-off between the behaviours associated with predator avoidance and mate acquisition in the mate-guarding amphipod crustacean Gammarus duebeni. We used laboratory experiments to investigate the impact of olfactory predator cues on activity, mate choice and mate-guarding behaviour of males and females. Pair formation declined under perceived risk of predation, reflecting reduced activity of both males and females and hence a reduced likelihood of encountering a mate. We also observed a reduction in the choosiness of both males and females. Under increased perceived predation risk, assessment of the female by the male was more likely to be followed by pair formation, and males showed a nonsignificant trend towards reduced discrimination in favour of large females and were less tenacious in their pair bond when they paired during exposure to predator cues. Females also showed less resistance behaviour, suggesting that both males and females trade off the costs of maximizing current reproductive success against the benefits of predator avoidance for survival and reproduction in the future. We discuss the implications of such context-dependent mating behaviours for ecological interactions between species and suggest that predators, via the effects of perceived predation risk on mate choice and mate guarding in the prey species, induce trait-mediated indirect effects with the potential to influence population dynamics and community structure. (C) 2008 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.

Genuine multipartite nonlocality of entangled thermal states

We assess quantum nonlocality of multiparty entangled thermal states by studying, quantitatively, both tripartite and quadripartite states belonging to the Greenberger-Horne-Zeilinger, W, and linear cluster-state classes and showing violation of relevant Bell-like inequalities. We discuss the conditions for maximizing the degree of violation against the local thermal character of the states and the inefficiency of the detection apparatuses. We demonstrate that such classes of multipartite entangled states can be made to last quite significantly, notwithstanding adverse operating conditions. This opens up the possibility for coherent exploitation of multipartite quantum channels made out of entangled thermal states. Our study is accompanied by a detailed description of possible generation schemes for the states analyzed.

Methodological considerations in reserve system selection: a case study of Malagasy Lemurs.

Although data quality and weighting decisions impact the outputs of reserve selection algorithms, these factors have not been closely studied. We examine these methodological issues in the use of reserve selection algorithms by comparing: (1) quality of input data and (2) use of different weighting methods for prioritizing among species. In 2003, the government of Madagascar, a global biodiversity hotspot, committed to tripling the size of its protected area network to protect 10% of the country’s total land area. We apply the Zonation reserve selection algorithm to distribution data for 52 lemur species to identify priority areas for the expansion of Madagascar’s reserve network. We assess the similarity of the areas selected, as well as the proportions of lemur ranges protected in the resulting areas when different forms of input data were used: extent of occurrence versus refined extent of occurrence. Low overlap between the areas selected suggests that refined extent of occurrence data are highly desirable, and to best protect lemur species, we recommend refining extent of occurrence ranges using habitat and altitude limitations. Reserve areas were also selected for protection based on three different species weighting schemes, resulting in marked variation in proportional representation of species among the IUCN Red List of Threatened Species extinction risk categories. This result demonstrates that assignment of species weights influences whether a reserve network prioritizes maximizing overall species protection or maximizing protection of the most threatened species.

Capacity and resource allocation of cooperative MIMO in ad hoc networks

Cooperative MIMO (Multiple Input–Multiple Output) allows multiple nodes share their antennas to emulate antenna arrays and transmit or receive cooperatively. It has the ability to increase the capacity for future wireless communication systems and it is particularly suited for ad hoc networks. In this study, based on the transmission procedure of a typical cooperative MIMO system, we first analyze the capacity of single-hop cooperative MIMO systems, and then we derive the optimal resource allocation strategy to maximize the end-to-end capacity in multi-hop cooperative MIMO systems. The study shows three implications. First, only when the intra-cluster channel is better than the inter-cluster channel, cooperative MIMO results in a capacity increment. Second, for a given scenario there is an optimal number of cooperative nodes. For instance, in our study an optimal deployment of three cooperative nodes achieve a capacity increment of 2 bps/Hz when compared with direct transmission. Third, an optimal resource allocation strategy plays a significant role in maximizing end-to-end capacity in multi-hop cooperative MIMO systems. Numerical results show that when optimal resource allocation is applied we achieve more than 20% end-to-end capacity increment in average when compared with an equal resource allocation strategy.

Promoting Health Behaviour Change in the Cardiac Patient

The importance of giving patients advice in relation to positive health behaviour change geared towards maximizing physical health must not be overlooked and is an important role of the cardiac nurse in contemporary health care. However, over-reliance on offering advice can often be counter-productive and less likely to achieve effective change. Patients are more likely to make positive health behaviour changes when they self-generate their own solutions to the health issues and problems that they face. In this article, the authors encourage cardiac nurses to embrace a less directive and more facilitative approach to patient health behaviour change in light of the mounting evidence that exists to the effectiveness of motivational interviewing techniques.

A hybrid metaheuristic approach to the university course timetabling problem

This paper describes the development of a novel metaheuristic that combines an electromagnetic-like mechanism (EM) and the great deluge algorithm (GD) for the University course timetabling problem. This well-known timetabling problem assigns lectures to specific numbers of timeslots and rooms maximizing the overall quality of the timetable while taking various constraints into account. EM is a population-based stochastic global optimization algorithm that is based on the theory of physics, simulating attraction and repulsion of sample points in moving toward optimality. GD is a local search procedure that allows worse solutions to be accepted based on some given upper boundary or ‘level’. In this paper, the dynamic force calculated from the attraction-repulsion mechanism is used as a decreasing rate to update the ‘level’ within the search process. The proposed method has been applied to a range of benchmark university course timetabling test problems from the literature. Moreover, the viability of the method has been tested by comparing its results with other reported results from the literature, demonstrating that the method is able to produce improved solutions to those currently published. We believe this is due to the combination of both approaches and the ability of the resultant algorithm to converge all solutions at every search process.