Variation in energy intake and basal metabolic rate of a bird migrating in a wind tunnel

1. We studied the changes in body mass, metabolizable energy intake rate (ME) and basal metabolic rate (BMR) of a Thrush Nightingale, Luscinia luscinia, following repeated 12-h migratory flights in a wind tunnel. In total the bird flew for 176 h corresponding to 6300 km. This is the first study where the fuelling phase has been investigated in a bird migrating in captivity.

2. ME was very high, supporting earlier findings that migrating birds have among the highest intake rates known among homeotherms. ME was significantly higher the second day of fuelling, indicating a build-up of the capacity of the digestive tract during the first day of fuelling.

3. Further indications of an increase in size or activity level of metabolically active structures during fuelling come from the short-term variation in BMR, which increased over the 2-day fuelling period with more than 20%, and in almost direct proportion to body mass. However, mass-specific BMR decreased over the season.

4. The patterns of mass change, ME and BMR of our focal bird following two occasions of 12-h fasts were the same as after flights, indicating that fast and flight may involve similar physiological processes.

5. The relatively low ME the first day following a flight may be a contributing factor to the well-known pattern that migrating birds during stopover normally lose mass the first day of fuelling.

Energy expenditure and personality in wild chipmunks

© 2015, Springer-Verlag Berlin Heidelberg. According to the “pace-of-life syndrome” concept, slow-fast life-history strategies favored under different ecological conditions should lead to co-adaptations between metabolic rate and personality traits such as activity, exploration, and boldness. Although the relationships between resting metabolic rate (RMR) and personality traits have been recently tested several times, we still do not know whether personality is related to the daily energy expenditure (DEE) of free-living individuals in their natural habitat. The objectives of this study were to assess the links between RMR, DEE, and two personality traits (exploration in an open-field and docility during handling) in wild eastern chipmunks (Tamias striatus). Using a multivariate mixed model, we found that exploration and docility were significantly correlated at the among-individual level, confirming the presence of a behavioral syndrome within our population. We also found that exploration, but not docility, was negatively correlated with DEE. Hence, fast explorers show lower DEE levels than slow explorers, independently of RMR and docility. This result adds to an increasingly large (and complex) literature reporting the impacts of personality traits on the biology, ecology, and physiology of animals in their natural environment.

Stochastic optimization models for energy management in carbonization process of carbon fiber production

Industrial producers face the task of optimizing production process in an attempt to achieve the desired quality such as mechanical properties with the lowest energy consumption. In industrial carbon fiber production, the fibers are processed in bundles containing (batches) several thousand filaments and consequently the energy optimization will be a stochastic process as it involves uncertainty, imprecision or randomness. This paper presents a stochastic optimization model to reduce energy consumption a given range of desired mechanical properties. Several processing condition sets are developed and for each set of conditions, 50 samples of fiber are analyzed for their tensile strength and modulus. The energy consumption during production of the samples is carefully monitored on the processing equipment. Then, five standard distribution functions are examined to determine those which can best describe the distribution of mechanical properties of filaments. To verify the distribution goodness of fit and correlation statistics, the Kolmogorov-Smirnov test is used. In order to estimate the selected distribution (Weibull) parameters, the maximum likelihood, least square and genetic algorithm methods are compared. An array of factors including the sample size, the confidence level, and relative error of estimated parameters are used for evaluating the tensile strength and modulus properties. The energy consumption and N2 gas cost are modeled by Convex Hull method. Finally, in order to optimize the carbon fiber production quality and its energy consumption and total cost, mixed integer linear programming is utilized. The results show that using the stochastic optimization models, we are able to predict the production quality in a given range and minimize the energy consumption of its industrial process.

Automated analysis of performance and energy consumption for cloud applications

In cloud environments, IT solutions are delivered to users via shared infrastructure. One consequence of this model is that large cloud data centres consume large amounts of energy and produce significant carbon footprints. A key objective of cloud providers is thus to develop resource provisioning and management solutions at minimum energy consumption while still guaranteeing Service Level Agreements (SLAs). However, a thorough understanding of both system performance and energy consumption patterns in complex cloud systems is imperative to achieve a balance of energy efficiency and acceptable performance. In this paper, we present StressCloud, a performance and energy consumption analysis tool for cloud systems. StressCloud can automatically generate load tests and profile system performance and energy consumption data. Using StressCloud, we have conducted extensive experiments to profile and analyse system performance and energy consumption with different types and mixes of runtime tasks. We collected finegrained energy consumption and performance data with different resource allocation strategies, system configurations and workloads. The experimental results show the correlation coefficients of energy consumption, system resource allocation strategies and workload, as well as the performance of the cloud applications. Our results can be used to guide the design and deployment of cloud applications to balance energy and performance requirements.

Energy efficient sleep schedule with service coverage guarantee in wireless sensor networks

Service oriented architecture has been proposed to support collaborations among distributed wireless sensor network (WSN) applications in an open dynamic environment. However, WSNs are resource constraint, and have limited computation abilities, limited communication bandwidth and especially limited energy. Fortunately, sensor nodes in WSNs are usually deployed redundantly, which brings the opportunity to adopt a sleep schedule for balanced energy consumption to extend the network lifetime. Due to miniaturization and energy efficiency, one sensor node can integrate several sense units and support a variety of services. Traditional sleep schedule considers only the constraints from the sensor nodes, can be categorized to a one-layer (i.e., node layer) issue. The service oriented WSNs should resolve the energy optimization issue considering the two-layer constraints, i.e., the sensor nodes layer and service layer. Then, the one-layer energy optimization scheme in previous work is not applicable for service oriented WSNs. Hence, in this paper we propose a sleep schedule with a service coverage guarantee in WSNs. Firstly, by considering the redundancy degree on both the service level and the node level, we can get an accurate redundancy degree of one sensor node. Then, we can adopt fuzzy logic to integrate the redundancy degree, reliability and energy to get a sleep factor. Based on the sleep factor, we furthermore propose the sleep mechanism. The case study and simulation evaluations illustrate the capability of our proposed approach.

Optimal coordination of G2V and V2G to support power grids with high penetration of renewable energy

Electric vehicles (EVs) have recently gained much popularity as a green alternative to fossil-fuel cars and a feasible solution to reduce air pollution in big cities. The use of EVs can also be extended as a demand response tool to support high penetration of renewable energy (RE) sources in future smart grid. Based on the certainty equivalent adaptive control (CECA) principle and a customer participation program, this paper presents a novel control strategy using optimization technique to coordinate not only the charging but also the discharging of EV batteries to deal with the intermittency in RE production. In addition, customer charging requirements and schedules are incorporated into the optimization algorithm to ensure customer satisfaction, and further improve the control performance. The merits of this scheme are its simplicity, efficiency, robustness and readiness for practical applications. The effectiveness of the proposed control algorithm is demonstrated by computer simulations of a power system with high level of wind energy integration.