Mobile cloud contextual awareness with the cloud personal assistant

This paper presents our efforts to bridge the gap between mobile context awareness, and mobile cloud services, using the Cloud Personal Assistant (CPA). The CPA is a part of the Context Aware Mobile Cloud Services (CAMCS) middleware, which we continue to develop. Specifically, we discuss the development and evaluation of the Context Processor component of this middleware. This component collects context data from the mobile devices of users, which is then provided to the CPA of each user, for use with mobile cloud services. We discuss the architecture and implementation of the Context Processor, followed by the evaluation. We introduce context profiles for the CPA, which influence its operation by using different context types. As part of the evaluation, we present two experimental context-aware mobile cloud services to illustrate how the CPA works with user context, and related context profiles, to complete tasks for the user.

Integrating mobile and cloud resources management using the cloud personal assistant

The mobile cloud computing model promises to address the resource limitations of mobile devices, but effectively implementing this model is difficult. Previous work on mobile cloud computing has required the user to have a continuous, high-quality connection to the cloud infrastructure. This is undesirable and possibly infeasible, as the energy required on the mobile device to maintain a connection, and transfer sizeable amounts of data is large; the bandwidth tends to be quite variable, and low on cellular networks. The cloud deployment itself needs to efficiently allocate scalable resources to the user as well. In this paper, we formulate the best practices for efficiently managing the resources required for the mobile cloud model, namely energy, bandwidth and cloud computing resources. These practices can be realised with our mobile cloud middleware project, featuring the Cloud Personal Assistant (CPA). We compare this with the other approaches in the area, to highlight the importance of minimising the usage of these resources, and therefore ensure successful adoption of the model by end users. Based on results from experiments performed with mobile devices, we develop a no-overhead decision model for task and data offloading to the CPA of a user, which provides efficient management of mobile cloud resources.

CloudNeg: An autonomous multi issue negotiation system, with preference elicitation component, for trading cloud services

Cloud services provide its users with flexible resource provisioning. But in the current market, a user has to choose from a limited set of configurations at a fixed price. This paper presents an autonomous negotiation system termed CloudNeg for negotiating cloud services. CloudNeg provides buyers and sellers of cloud services with autonomous agents to negotiate on the specifications of a cloud instance, including price, on their behalf. These agents elicit their buyers’ time preferences and use them in negotiations. Further, this paper presents two artifacts: a negotiation algorithm and a prototype which together form CloudNeg.

Delivering mobile cloud services to the user: description, discovery, and consumption

The mobile cloud computing paradigm can offer relevant and useful services to the users of smart mobile devices. Such public services already exist on the web and in cloud deployments, by implementing common web service standards. However, these services are described by mark-up languages, such as XML, that cannot be comprehended by non-specialists. Furthermore, the lack of common interfaces for related services makes discovery and consumption difficult for both users and software. The problem of service description, discovery, and consumption for the mobile cloud must be addressed to allow users to benefit from these services on mobile devices. This paper introduces our work on a mobile cloud service discovery solution, which is utilised by our mobile cloud middleware, Context Aware Mobile Cloud Services (CAMCS). The aim of our approach is to remove complex mark-up languages from the description and discovery process. By means of the Cloud Personal Assistant (CPA) assigned to each user of CAMCS, relevant mobile cloud services can be discovered and consumed easily by the end user from the mobile device. We present the discovery process, the architecture of our own service registry, and service description structure. CAMCS allows services to be used from the mobile device through a user's CPA, by means of user defined tasks. We present the task model of the CPA enabled by our solution, including automatic tasks, which can perform work for the user without an explicit request.

Artificial opal photonic crystals and inverse opal structures - fundamentals and applications from optics to energy storage

Photonic crystals (PhCs) influence the propagation of light by their periodic variation in dielectric contrast or refractive index. This review outlines the attractive optical qualities inherent to most PhCs namely the presence of full or partial photonic band gaps and the possibilities they present towards the inhibition of spontaneous emission and the localization of light. Colloidal self-assembly of polymer or silica spheres is one of the most favoured and low cost methods for the formation of PhCs as artificial opals. The state of the art in growth methods currently used for colloidal self-assembly are discussed and the use of these structures for the formation of inverse opal architectures is then presented. Inverse opal structures with their porous and interconnected architecture span several technological arenas - optics and optoelectronics, energy storage, communications, sensor and biological applications. This review presents several of these applications and an accessible overview of the physics of photonic crystal optics that may be useful for opal and inverse opal researchers in general, with a particular emphasis on the recent use of these three-dimensional porous structures in electrochemical energy storage technology. Progress towards all-optical integrated circuits may lie with the concepts of the photonic crystal, but the unique optical and structural properties of these materials and the convergence of PhC and energy storage disciplines may facilitate further developments and non-destructive optical analysis capabilities for (electro)chemical processes that occur within a wide variety of materials in energy storage research.

Human milk banking: Influence of storage processes and of bacterial contamination on some milk constituents

SCOPUS: ar.j

Analysis of educational materials and destruction/opt-out initiatives for storage and use of residual newborn screening samples.

In recent years, the storage and use of residual newborn screening (NBS) samples has gained attention. To inform ongoing policy discussions, this article provides an update of previous work on new policies, educational materials, and parental options regarding the storage and use of residual NBS samples. A review of state NBS Web sites was conducted for information related to the storage and use of residual NBS samples in January 2010. In addition, a review of current statutes and bills introduced between 2005 and 2009 regarding storage and/or use of residual NBS samples was conducted. Fourteen states currently provide information about the storage and/or use of residual NBS samples. Nine states provide parents the option to request destruction of the residual NBS sample after the required storage period or the option to exclude the sample for research uses. In the coming years, it is anticipated that more states will consider policies to address parental concerns about the storage and use of residual NBS samples. Development of new policies regarding storage and use of residual NBS samples will require careful consideration of impact on NBS programs, parent and provider educational materials, and respect for parents among other issues.

NMR methods for characterizing the pore structures and hydrogen storage properties of microporous carbons.

(1)H NMR spectroscopy is used to investigate a series of microporous activated carbons derived from a poly(ether ether ketone) (PEEK) precursor with varying amounts of burnoff (BO). In particular, properties relevant to hydrogen storage are evaluated such as pore structure, average pore size, uptake, and binding energy. High-pressure NMR with in situ H(2) loading is employed with H(2) pressure ranging from 100 Pa to 10 MPa. An N(2)-cooled cryostat allows for NMR isotherm measurements at both room temperature ( approximately 290 K) and 100 K. Two distinct (1)H NMR peaks appear in the spectra which represent the gaseous H(2) in intergranular pores and the H(2) residing in micropores. The chemical shift of the micropore peak is observed to evolve with changing pressure, the magnitude of this effect being correlated to the amount of BO and therefore the structure. This is attributed to the different pressure dependence of the amount of adsorbed and non-adsorbed molecules within micropores, which experience significantly different chemical shifts due to the strong distance dependence of the ring current effect. In pores with a critical diameter of 1.2 nm or less, no pressure dependence is observed because they are not wide enough to host non-adsorbed molecules; this is the case for samples with less than 35% BO. The largest estimated pore size that can contribute to the micropore peak is estimated to be around 2.4 nm. The total H(2) uptake associated with pores of this size or smaller is evaluated via a calibration of the isotherms, with the highest amount being observed at 59% BO. Two binding energies are present in the micropores, with the lower, more dominant one being on the order of 5 kJ mol(-1) and the higher one ranging from 7 to 9 kJ mol(-1).

Physical and economic potential of geological CO2 storage in saline aquifers.

Carbon sequestration in sandstone saline reservoirs holds great potential for mitigating climate change, but its storage potential and cost per ton of avoided CO2 emissions are uncertain. We develop a general model to determine the maximum theoretical constraints on both storage potential and injection rate and use it to characterize the economic viability of geosequestration in sandstone saline aquifers. When applied to a representative set of aquifer characteristics, the model yields results that compare favorably with pilot projects currently underway. Over a range of reservoir properties, maximum effective storage peaks at an optimal depth of 1600 m, at which point 0.18-0.31 metric tons can be stored per cubic meter of bulk volume of reservoir. Maximum modeled injection rates predict minima for storage costs in a typical basin in the range of $2-7/ ton CO2 (2005 U.S.$) depending on depth and basin characteristics in our base-case scenario. Because the properties of natural reservoirs in the United States vary substantially, storage costs could in some cases be lower or higher by orders of magnitude. We conclude that available geosequestration capacity exhibits a wide range of technological and economic attractiveness. Like traditional projects in the extractive industries, geosequestration capacity should be exploited starting with the low-cost storage options first then moving gradually up the supply curve.

Recognition of Planar Segments in Point Cloud Based on Wavelet Transform

© 2005-2012 IEEE.Within industrial automation systems, three-dimensional (3-D) vision provides very useful feedback information in autonomous operation of various manufacturing equipment (e.g., industrial robots, material handling devices, assembly systems, and machine tools). The hardware performance in contemporary 3-D scanning devices is suitable for online utilization. However, the bottleneck is the lack of real-time algorithms for recognition of geometric primitives (e.g., planes and natural quadrics) from a scanned point cloud. One of the most important and the most frequent geometric primitive in various engineering tasks is plane. In this paper, we propose a new fast one-pass algorithm for recognition (segmentation and fitting) of planar segments from a point cloud. To effectively segment planar regions, we exploit the orthonormality of certain wavelets to polynomial function, as well as their sensitivity to abrupt changes. After segmentation of planar regions, we estimate the parameters of corresponding planes using standard fitting procedures. For point cloud structuring, a z-buffer algorithm with mesh triangles representation in barycentric coordinates is employed. The proposed recognition method is tested and experimentally validated in several real-world case studies.

The upstream enhancer elements of the G6PC promoter are critical for optimal G6PC expression in murine glycogen storage disease type Ia.

Glycogen storage disease type-Ia (GSD-Ia) patients deficient in glucose-6-phosphatase-α (G6Pase-α or G6PC) manifest impaired glucose homeostasis characterized by fasting hypoglycemia, growth retardation, hepatomegaly, nephromegaly, hyperlipidemia, hyperuricemia, and lactic acidemia. Two efficacious recombinant adeno-associated virus pseudotype 2/8 (rAAV8) vectors expressing human G6Pase-α have been independently developed. One is a single-stranded vector containing a 2864-bp of the G6PC promoter/enhancer (rAAV8-GPE) and the other is a double-stranded vector containing a shorter 382-bp minimal G6PC promoter/enhancer (rAAV8-miGPE). To identify the best construct, a direct comparison of the rAAV8-GPE and the rAAV8-miGPE vectors was initiated to determine the best vector to take forward into clinical trials. We show that the rAAV8-GPE vector directed significantly higher levels of hepatic G6Pase-α expression, achieved greater reduction in hepatic glycogen accumulation, and led to a better toleration of fasting in GSD-Ia mice than the rAAV8-miGPE vector. Our results indicated that additional control elements in the rAAV8-GPE vector outweigh the gains from the double-stranded rAAV8-miGPE transduction efficiency, and that the rAAV8-GPE vector is the current choice for clinical translation in human GSD-Ia.

On the modelling of the thermal interactions between a spray curtain and an impinging cold gas cloud

A mixed Lagrangian-Eulerian model of a Water Curtain barrier is presented. The heat, mass and momentum processes are modelled in a Lagrangian framework for the dispersed phase and in an Eulerian framework for the carrier phase. The derivation of the coupling source terms is illustrated with reference to a given carrier phase cell. The turbulent character of the flow is treated with a single equation model, modified to directly account for the influence of the particles on the flow. The model is implemented in the form of a 2 D incompressible Navier Stokes solver, coupled to an adaptive Rung Kutta method for the Lagrangian sub-system. Simulations of a free standing full cone water spray show satisfactory agreement with experiment. Predictions of a Water Curtain barrier impacted by a cold gas cloud point to markedly different flow fields for the upward and downward configurations, which could influence the effectiveness of chemical absorption in the liquid phase.

Application of simulation technologies in the analysis of granular material behaviour during transport and storage

Problems in the preservation of the quality of granular material products are complex and arise from a series of sources during transport and storage. In either designing a new plant or, more likely, analysing problems that give rise to product quality degradation in existing operations, practical measurement and simulation tools and technologies are required to support the process engineer. These technologies are required to help in both identifying the source of such problems and then designing them out. As part of a major research programme on quality in particulate manufacturing computational models have been developed for segregation in silos, degradation in pneumatic conveyors, and the development of caking during storage, which use where possible, micro-mechanical relationships to characterize the behaviour of granular materials. The objective of the work presented here is to demonstrate the use of these computational models of unit processes involved in the analysis of large-scale processes involving the handling of granular materials. This paper presents a set of simulations of a complete large-scale granular materials handling operation, involving the discharge of the materials from a silo, its transport through a dilute-phase pneumatic conveyor, and the material storage in a big bag under varying environmental temperature and humidity conditions. Conclusions are drawn on the capability of the computational models to represent key granular processes, including particle size segregation, degradation, and moisture migration caking.

The UK WTC 9/11 evacuation study: methodologies used in the elicitation and storage of human factors data

This paper describes the methodologies employed in the collection and storage of first-hand accounts of evacuation experiences derived from face-to-face interviews with evacuees from the World Trade Center (WTC) Twin Towers complex on 11 September 2001. In particular the paper describes the development of the High-rise Evacuation Evaluation Database (HEED). This is a flexible qualitative research tool which contains the full transcribed interview accounts and coded evacuee experiences extracted from those transcripts. The data and information captured and stored in the HEED database is not only unique, but it provides a means to address current and emerging issues relating to human factors associated with the evacuation of high-rise buildings.

Methodologies employed in the collection, retrieval and storage of human factors information derived from first hand accounts of survivors of the WTC disaster of 11 September 2001

This article provides a broad overview of project HEED (High-rise Evacuation Evaluation Database) and the methodologies employed in the collection and storage of first-hand accounts of evacuation experiences derived from face-to-face interviews of evacuees from the World Trade Center (WTC) Twin Towers complex on September 11, 2001. In particular, the article describes the development of the HEED database. This is a flexible research tool which contains qualitative type data in the form of coded evacuee experiences along with the full interview transcripts. The data and information captured and stored in the HEED database is not only unique, but provides a means to address current and emerging issues relating to human factors associated with the evacuation of high-rise buildings