Energy conservation in wireless sensor networks: a rule-based approach

The research reported in this paper addresses the problem of energy conservation in wireless sensor networks (WSNs). It proposes concepts and techniques to extract environmental information that are useful for controlling sensor operations, in order to enable sensor nodes to conserve their energy, and consequently prolong the network lifetime. These concepts and techniques are consolidated in a generic framework we term CASE: Context Awareness in Sensing Environments framework. CASE targets energy conservation at the network level. A subset framework of CASE, we term CASE Compact, targets energy conservation at the sensor node level. In this paper, we elaborate on these two frameworks, elucidate the requirements for them to operate together within a WSN and evaluate the applications they can be applied to for energy conservation.     

Electroplated CoPt magnets for actuation of stiff cantilevers

A cantilever has been microfabricated for use in non-contact Atomic Force Microscopy (AFM) using a very thick magnetic film to actuate the cantilever motion. The thick magnetic block is deposited electrochemically over a defined area of the cantilever. This cantilever is particularly suitable for driving stiff AFM cantilevers in a liquid environment. Clean mechanical resonances are easily observed. Examples are given of a hard (CoPt) magnet of dimension 29 × 21 × 6 μm(3) electroplated on Silicon cantilevers of stiffness ~22 N/m, giving a static displacement of ~0.2 nm in an applied field of 10(-3) T.     

Design optimization and fatigue testing of an electronically-driven mechanically-resonant cantilever spring mechanism

A light scanning device consisting of an electronically-driven mechanically-resonant cantilever spring-mirror system has been developed for innovative lighting applications. The repeated flexing of the cantilever spring during operation can lead to premature fatigue failure. A model was created to optimize the spring design. The optimized spring design can reduce stress by approximately one-third from the initial design. Fatigue testing showed that the optimized spring design can operate continuously for over 1 month without failure. Analysis of failures indicates surface cracks near the root of the spring are responsible for the failures.     

CUGatesDensity—Quantum circuit analyser extended to density matrices

CUGatesDensity is an extension of the original quantum circuit analyser CUGates (Loke and Wang, 2011) [7] to provide explicit support for the use of density matrices. The new package enables simulation of quantum circuits involving statistical ensemble of mixed quantum states. Such analysis is of vital importance in dealing with quantum decoherence, measurements, noise and error correction, and fault tolerant computation. Several examples involving mixed state quantum computation are presented to illustrate the use of this package.     

Scalable Fabrication of Porous Microchannel Nerve Guidance Scaffolds with Complex Geometries

Microchannel scaffolds accelerate nerve repair by guiding growing neuronal processes across injury sites. Although geometry, materials chemistry, stiffness, and porosity have been shown to influence nerve growth within nerve guidance scaffolds, independent tuning of these properties in a high‐throughput manner remains a challenge. Here, fiber drawing is combined with salt leaching to produce microchannels with tunable cross sections and porosity. This technique is applicable to an array of biochemically inert polymers, and it delivers hundreds of meters of porous microchannel fibers. Employing these fibers as filaments during 3D printing enables the production of microchannel scaffolds with geometries matching those of biological nerves, including branched topographies. Applied to sensory neurons, fiber‐based porous microchannels enhance growth as compared to non‐porous channels with matching materials and geometries. The combinatorial scaffold fabrication approach may advance the studies of neural regeneration and accelerate the development of nerve repair devices.     

Waste to energy: the effects of <Emphasis Type="Italic">Pseudomonas</Emphasis> sp. on <Emphasis Type="Italic">Chlorella sorokiniana</Emphasis> biomass and lipid productions in palm oil mill effluent

Microalgae are recognised as promising feedstock for biofuel production. The feasibility in commercial scale microalgae cultivation could be enhanced by incorporating palm oil mill effluent (POME) as culture medium, for greater biomass growth and lipid production, together with POME bioremediation. The polluting POME is generated massively in Malaysia. POME contains high concentrations of carbon and nutrients, thus it is suitable to be applied for microalgae cultivation. The approach on waste to energy should be advanced. We studied the effects of applying Pseudomonas sp. on Chlorella sorokiniana CY-1 cultivation in POME. Pseudomonas sp. was found effective in POME decolourisation prior to C. sorokiniana CY-1 cultivation. Yet, microalgae biomass and lipid productions were higher in the non-decolourised POME. Pseudomonas sp. was as well-being co-cultivated with C. sorokiniana CY-1 in ratios of microalgae versus bacteria of 1:1; 2:1 and 1:2. Biomass of 2.04 g L^?1 and biomass productivity of 185.71 mg L^?1 d^?1 were attained in ratio of 1:1. Interestingly, the lipid content exhibited was excellent (16.04%), and about twofold higher than other ratios and the control (without bacteria). Fatty acids compositions were dominated by C16:0 (32.49%), C18:1 (24.06%) and C18:2 (20.28%), which were desirable fatty acids for biodiesel production. Effective POME bioremediation achieved with chemical oxygen demand, total nitrogen and total phosphorus removal of 53.7, 55.6 and 77.3%, respectively. Co-cultivation of microalgae and bacteria can be applied in the POME treatment plant. This allows satisfactory biomass and excellent lipid yields for biofuel production, as well as effective wastewater bioremediation.     

Explanation-aware service selection: rationale and reputation

A collection of 1 billion¹ publicly available web services can form an internet-scale infrastructure for building diverse applications. For a given application, selection of services and service providers from this collection becomes important and reputation is recognized as a key factor for this purpose. However, current reputation systems are limited in their ability to exchange reputation information between heterogeneous systems. To facilitate meaningful exchange and reuse of reputation information and for the overall determination of reputation, we identify the need to infer and explicate rationale for ratings. We present our knowledge-based approach to inferring and explicating rationale for ratings. We show that this approach facilitates detection of deception and collusion, user preference elicitation, explication of rationale behind user ratings and generation of personalized service recommendations.     

Adapting the mobile phone for task efficiency: the case of predicting outgoing calls using frequency and regularity of historical calls

The set of functionalities provided by advanced mobile phones is significantly increasing. However, the small size of mobile phone user interfaces makes it difficult for the user to deal with this large number of functionalities, which could reflect negatively on user performance and the efficiency of mobile phone functionalities. In this paper, we designed and developed an adaptive task-based functionality called ATF on mobile phones, where the task we focused on was to predict the next contact that the user is most likely to call. Furthermore, we conducted comprehensive evaluation of our approach. We show that our approach can successfully predict contacts that a user will most likely call next. Our results uncover the frequency and pattern of regularity in making mobile phone calls and suggest promising avenues for future work for optimising tasks (beyond phone calls) performed with the mobile phone.     

Quick integrative optimizers for minimizing the error of neural computing in pan evaporation modeling

To achieve an efficient methodology for approximating pan evaporation (E_P), this study offers two metaheuristic-integrated predictors. Shuffled complex evolution (SCE) and electromagnetic field optimization (EFO) are two of the fastest metaheuristic algorithms that are synthesized with artificial neural network (ANN). By doing this, the ANN is optimized in a noticeably shorter time compared to its integration with other metaheuristic techniques. Five-year climatic data of the Bakersfield station (California, USA) with an 80:20 ratio are used for developing and testing the methods. The proposed hybrids are implemented with appropriate population sizes (20 and 35 for the SCE and EFO, respectively) and their results are compared to a single ANN. Accuracy evaluation (correlation coefficients > 0.99) professed that the neural network with both conventional and sophisticated trainers is a competent approach for the E_P simulation. Besides, it was observed that the error of prediction by the ANN-SCE and ANN-EFO is 6.02 and 9.27% lower than the single ANN, respectively. Therefore, the used strategies can enhance the applicability of the ANN. The time elapsed in the optimization using SCE and EFO was 479.0 and 281.9 s, respectively. A comparison between these algorithms revealed that the EFO is both a faster and more accurate optimizer. The ANN-EFO is accordingly recommended as a new efficient model for predicting the E_P.