Test of a mediation model of perceived organisational support

This study examined the mediating influence of trust in organization (TIO) and organization-based self-esteem (OBSE) on the relationship between perceived organization support (POS) and its work outcomes. Data were obtained from employee–supervisor dyads from multiple organizations located in a major city in southern China. Structural equation modeling results revealed that: (a) POS related to TIO and OBSE and (b) TIO and OBSE fully mediated the relationship between POS and the work outcomes of organizational commitment and in-role performance, but partially mediated the POS–organizational citizenship behavior relationship.

Improved lithium storage properties of electrospun TiO2 with tunable morphology:from porous anatase to necklace rutile

Three-dimensional TiO2 with tunable morphology and crystalline phase was successfully prepared by the electrospinning technique and subsequent annealing. Porous-shaped anatase TiO2, cluster-shaped anatase TiO2, hierarchical-shaped rutile (minor) TiO2 and nano-necklace rutile (major) TiO2 were achieved at 500, 600, 700 and 800°C, respectively. The mechanism of the formation of these tailored morphologies and crystallinity was investigated. Lithium insertion properties were evaluated by galvanostatic and potentiostatic modes in half-cell configurations. By combining the large surface area, open mesoporosity and stable crystalline phase, the porous-shaped anatase TiO2 exhibited the highest capacity, best rate and cycling performance among the four samples. The present results demonstrated the usefulness of three-dimensional TiO 2 as an anode for lithium storage with improved electrode performance. © 2013 The Royal Society of Chemistry.

A utility model for volunteered service composition

Volunteered Service Composition (VSC) refers to the process of composing volunteered services and resources. These services are typically published to a pool of voluntary resources. The composition aims at satisfying some objectives (e.g. Utilizing storage and eliminating waste, sharing space and optimizing for energy, reducing computational cost etc.). In cases when a single volunteered service does not satisfy a request, VSC will be required. In this paper, we contribute to three approaches for composing volunteered services: these are exhaustive, naïve and utility-based search approach to VSC. The proposed new utility-based approach, for instance, is based on measuring the utility that each volunteered service can provide to each request and systematically selects the one with the highest utility. We found that the utility-based approach tend to be more effective and efficient when selecting services, while minimizing resource waste when compared to the other two approaches.

Self-adaptive volunteered services composition through stimulus-and time-awareness

Volunteered Service Composition (VSC) refers to the process of composing volunteered services and resources. These services are typically published to a pool of voluntary resources. Selection and composition decisions tend to encounter numerous uncertainties: service consumers and applications have little control of these services and tend to be uncertain about their level of support for the desired functionalities and non-functionalities. In this paper, we contribute to a self-awareness framework that implements two levels of awareness, Stimulus-awareness and Time-awareness. The former responds to basic changes in the environment while the latter takes into consideration the historical performance of the services. We have used volunteer service computing as an example to demonstrate the benefits that self-awareness can introduce to self-adaptation. We have compared the Stimulus-and Time-awareness approaches with a recent Ranking approach from the literature. The results show that the Time-awareness level has the advantage of satisfying higher number of requests with lower time cost.

Does money matter in inflation forecasting?

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two nonlinear techniques, namely, recurrent neural networks and kernel recursive least squares regressiontechniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a nave random walk model. The best models were nonlinear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation. Beyond its economic findings, our study is in the tradition of physicists' long-standing interest in the interconnections among statistical mechanics, neural networks, and related nonparametric statistical methods, and suggests potential avenues of extension for such studies. © 2010 Elsevier B.V. All rights reserved.