Application of theory in developing peer-support based intervention to improve self-management for cardiac patients with diabetes

The biosafety of carbon nanomaterial needs to be critically evaluated with both experimental and theoretical validations before extensive biomedical applications. In this letter, we present an analysis of the binding ability of two dimensional monolayer carbon nanomaterial on actin by molecular simulation to understand their adhesive characteristics on F-actin cytoskeleton. The modelling results indicate that the positively charged carbon nanomaterial has higher binding stability on actin. Compared to crystalline graphene, graphene oxide shows higher binding influence on actin when carrying 11 positive surface charge. This theoretical investigation provides insights into the sensitivity of actin-related cellular activities on carbon nanomaterial.

Magnetic zeolite NaA : Synthesis, characterization based on metakaolin and its application for the removal of Cu2+, Pb2+

The optimum parameters for synthesis of zeolite NaA based on metakaolin were investigated according to results of cation exchange capacity and static water adsorption of all synthesis products and selected X-ray diffraction (XRD). Magnetic zeolite NaA was synthesized by adding Fe3O4 in the precursor of zeolite. Zeolite NaA and magnetic zeolite NaA were characterized with scanning electron microscopy (SEM) and XRD. Magnetic zeolite NaA with different Fe3O4 loadings was prepared and used for removal of heavy metals (Cu2+, Pb2+). The results show the optimum parameters for synthesis zeolite NaA are SiO2/Al2O3 = 2.3, Na2O/SiO2 = 1.4, H2O/Na2O = 50, crystallization time 8 h, crystallization temperature 95 �C. The addition of Fe3O4 makes the NaA zeolite with good magnetic susceptibility and good magnetic stability regardless of the Fe3O4 loading, confirming the considerable separation efficiency. Additionally, Fe3O4 loading had a little effect on removal of heavy metal by magnetic zeolite, however, the adsorption capacity still reaches 2.3 mmol g�1 for Cu2+, Pb2+ with a removal efficiency of over 95% in spite of 4.7% Fe3O4 loading. This indicates magnetic zeolite can be used to remove metal heavy at least Cu2+, Pb2+ from water with metallic contaminants and can be separated easily after a magnetic process.

Image based visual servo control for fixed wing UAVs tracking linear infrastructure in wind

This paper presents an Image Based Visual Servo control design for Fixed Wing Unmanned Aerial Vehicles tracking locally linear infrastructure in the presence of wind using a body fixed imaging sensor. Visual servoing offers improved data collection by posing the tracking task as one of controlling a feature as viewed by the inspection sensor, although is complicated by the introduction of wind as aircraft heading and course angle no longer align. In this work it is shown that the effects of wind alter the desired line angle required for continuous tracking to equal the wind correction angle as would be calculated to set a desired course. A control solution is then sort by linearizing the interaction matrix about the new feature pose such that kinematics of the feature can be augmented with the lateral dynamics of the aircraft, from which a state feedback control design is developed. Simulation results are presented comparing no compensation, integral control and the proposed controller using the wind correction angle, followed by an assessment of response to atmospheric disturbances in the form of turbulence and wind gusts

A new research agenda into community-based protest in construction

Many countries face enormous development challenges in adapting to demographic change, urbanisation and emerging issues such as housing affordability and climate change. These challenges are best resolved in consultation with communities rather than in conflict with them. A rich tradition of research and intellectual frameworks exist in the fields of urban geography and planning to understand and manage community concerns during the pre-development approval stages of new projects. However current theoretical frameworks are inadequate in construction management and a new research agenda is needed to develop conceptual frameworks to guide thinking about the role of communities in the construction process. By discussing the components of such a model, it is concluded that this would require a fundamental shift in thinking which challenges traditional structuralist paradigms. A new constructivist paradigm is presented that conceives community consultation as a negotiation process which does not stop at the pre-development planning stages but which continues over the entire life of a project.

The use of a Riesz fractional differential-based approach for texture enhancement in image processing

Abstract: Texture enhancement is an important component of image processing, with extensive application in science and engineering. The quality of medical images, quantified using the texture of the images, plays a significant role in the routine diagnosis performed by medical practitioners. Previously, image texture enhancement was performed using classical integral order differential mask operators. Recently, first order fractional differential operators were implemented to enhance images. Experiments conclude that the use of the fractional differential not only maintains the low frequency contour features in the smooth areas of the image, but also nonlinearly enhances edges and textures corresponding to high-frequency image components. However, whilst these methods perform well in particular cases, they are not routinely useful across all applications. To this end, we applied the second order Riesz fractional differential operator to improve upon existing approaches of texture enhancement. Compared with the classical integral order differential mask operators and other fractional differential operators, our new algorithms provide higher signal to noise values, which leads to superior image quality.

A pre-treatment verification tool for IMRT based on EPID measurements and a GEANT4 back projection application

The aim of this work is to develop software that is capable of back projecting primary fluence images obtained from EPID measurements through phantom and patient geometries in order to calculate 3D dose distributions. In the first instance, we aim to develop a tool for pretreatment verification in IMRT. In our approach, a Geant4 application is used to back project primary fluence values from each EPID pixel towards the source. Each beam is considered to be polyenergetic, with a spectrum obtained from Monte Carlo calculations for the LINAC in question. At each step of the ray tracing process, the energy differential fluence is corrected for attenuation and beam divergence. Subsequently, the TERMA is calculated and accumulated to an energy differential 3D TERMA distribution. This distribution is then convolved with monoenergetic point spread kernels, thus generating energy differential 3D dose distributions. The resulting dose distributions are accumulated to yield the total dose distribution, which can then be used for pre-treatment verification of IMRT plans. Preliminary results were obtained for a test EPID image comprised of 100 9 100 pixels of unity fluence. Back projection of this field into a 30 cm9 30 cm 9 30 cm water phantom was performed, with TERMA distributions obtained in approximately 10 min (running on a single core of a 3 GHz processor). Point spread kernels for monoenergetic photons in water were calculated using a separate Geant4 application. Following convolution and summation, the resulting 3D dose distribution produced familiar build-up and penumbral features. In order to validate the dose model we will use EPID images recorded without any attenuating material in the beam for a number of MLC defined square fields. The dose distributions in water will be calculated and compared to TPS predictions.

Stigmergy within web modelling languages : positive feedback mechanisms

Stigmergy is a biological term originally used when discussing insect or swarm behaviour, and describes a model supporting environment-based communication separating artefacts from agents. This phenomenon is demonstrated in the behavior of ants and their food foraging supported by pheromone trails, or similarly termites and their termite nest building process. What is interesting with this mechanism is that highly organized societies are formed without an apparent central management function. We see design features in Web sites that mimic stigmergic mechanisms as part of the User Interface and we have created generalizations of these patterns. Software development and Web site development techniques have evolved significantly over the past 20 years. Recent progress in this area proposes languages to model web applications to facilitate the nuances specific to these developments. These modeling languages provide a suitable framework for building reusable components encapsulating our design patterns of stigmergy. We hypothesize that incorporating stigmergy as a separate feature of a site’s primary function will ultimately lead to enhanced user coordination.

A multi-layered approach for site detection in UAS emergency landing scenarios using geometry-based image segmentation

This paper presents an alternative approach to image segmentation by using the spatial distribution of edge pixels as opposed to pixel intensities. The segmentation is achieved by a multi-layered approach and is intended to find suitable landing areas for an aircraft emergency landing. We combine standard techniques (edge detectors) with novel developed algorithms (line expansion and geometry test) to design an original segmentation algorithm. Our approach removes the dependency on environmental factors that traditionally influence lighting conditions, which in turn have negative impact on pixel-based segmentation techniques. We present test outcomes on realistic visual data collected from an aircraft, reporting on preliminary feedback about the performance of the detection. We demonstrate consistent performances over 97% detection rate.

Vision based control for micro aerial vehicles : Application to sense and avoid

This work presents a collision avoidance approach based on omnidirectional cameras that does not require the estimation of range between two platforms to resolve a collision encounter. Our method achieves minimum separation between the two vehicles involved by maximising the view-angle given by the omnidirectional sensor. Only visual information is used to achieve avoidance under a bearing- only visual servoing approach. We provide theoretical problem formulation, as well as results from real flights using small quadrotors

A UKF-based estimation strategy for actuator fault detection of UASs

This paper presents a recursive strategy for online detection of actuator faults on a unmanned aerial system (UAS) subjected to accidental actuator faults. The proposed detection algorithm aims to provide a UAS with the capability of identifying and determining characteristics of actuator faults, offering necessary flight information for the design of fault-tolerant mechanism to compensate for the resultant side-effect when faults occur. The proposed fault detection strategy consists of a bank of unscented Kalman filters (UKFs) with each one detecting a specific type of actuator faults and estimating correspond- ing velocity and attitude information. Performance of the proposed method is evaluated using a typical nonlinear UAS model and it is demonstrated in simulations that our method is able to detect representative faults with a sufficient accuracy and acceptable time delay, and can be applied to the design of fault-tolerant flight control systems of UASs.

Importance of sustainability-related cost components in highway infrastructure : perspective of stakeholders in Australia

Highway infrastructure development typically requires major capital input. Unless planned properly, such requirements can cause serious financial constraints for investors. The push for sustainability adds a new dimension to the complexity of evaluating highway projects. Finding environmentally and socially responsible solutions for highway construction will improve its potential for acceptance by the society and in many instances the infrastructure's life span. Even so, the prediction and determination of a project's long-term financial viability can be a precarious exercise. Existing studies in this area have not indicated details of how to identify and deal with costs incurred in pursuing sustainability measures in highway infrastructure. This paper provides insights into the major challenges of implementing sustainability in highway project development in terms of financial concerns and obligations. It discusses the results from recent research through a literature study and a questionnaire survey of key industry stakeholders involved in highway infrastructure development. The research identified critical cost components relating to sustainability measures based on perspectives of industry stakeholders. All stakeholders believe sustainability related costs are an integral part of the decision making. However, the importance rating of these costs is relative to each stakeholder's core business objectives. This will influence the way these cost components are dealt with during the evaluation of highway investment alternatives and financial implications. This research encourages positive thinking among the highway infrastructure practitioners about sustainability. It calls for the construction industry to maximise sustainability deliverables while ensuring financial viability over the life cycle of highway infrastructure projects.

Composite SaaS resource management in cloud computing using evolutionary computation

Cloud computing is an emerging computing paradigm in which IT resources are provided over the Internet as a service to users. One such service offered through the Cloud is Software as a Service or SaaS. SaaS can be delivered in a composite form, consisting of a set of application and data components that work together to deliver higher-level functional software. SaaS is receiving substantial attention today from both software providers and users. It is also predicted to has positive future markets by analyst firms. This raises new challenges for SaaS providers managing SaaS, especially in large-scale data centres like Cloud. One of the challenges is providing management of Cloud resources for SaaS which guarantees maintaining SaaS performance while optimising resources use. Extensive research on the resource optimisation of Cloud service has not yet addressed the challenges of managing resources for composite SaaS. This research addresses this gap by focusing on three new problems of composite SaaS: placement, clustering and scalability. The overall aim is to develop efficient and scalable mechanisms that facilitate the delivery of high performance composite SaaS for users while optimising the resources used. All three problems are characterised as highly constrained, large-scaled and complex combinatorial optimisation problems. Therefore, evolutionary algorithms are adopted as the main technique in solving these problems. The first research problem refers to how a composite SaaS is placed onto Cloud servers to optimise its performance while satisfying the SaaS resource and response time constraints. Existing research on this problem often ignores the dependencies between components and considers placement of a homogenous type of component only. A precise problem formulation of composite SaaS placement problem is presented. A classical genetic algorithm and two versions of cooperative co-evolutionary algorithms are designed to now manage the placement of heterogeneous types of SaaS components together with their dependencies, requirements and constraints. Experimental results demonstrate the efficiency and scalability of these new algorithms. In the second problem, SaaS components are assumed to be already running on Cloud virtual machines (VMs). However, due to the environment of a Cloud, the current placement may need to be modified. Existing techniques focused mostly at the infrastructure level instead of the application level. This research addressed the problem at the application level by clustering suitable components to VMs to optimise the resource used and to maintain the SaaS performance. Two versions of grouping genetic algorithms (GGAs) are designed to cater for the structural group of a composite SaaS. The first GGA used a repair-based method while the second used a penalty-based method to handle the problem constraints. The experimental results confirmed that the GGAs always produced a better reconfiguration placement plan compared with a common heuristic for clustering problems. The third research problem deals with the replication or deletion of SaaS instances in coping with the SaaS workload. To determine a scaling plan that can minimise the resource used and maintain the SaaS performance is a critical task. Additionally, the problem consists of constraints and interdependency between components, making solutions even more difficult to find. A hybrid genetic algorithm (HGA) was developed to solve this problem by exploring the problem search space through its genetic operators and fitness function to determine the SaaS scaling plan. The HGA also uses the problem's domain knowledge to ensure that the solutions meet the problem's constraints and achieve its objectives. The experimental results demonstrated that the HGA constantly outperform a heuristic algorithm by achieving a low-cost scaling and placement plan. This research has identified three significant new problems for composite SaaS in Cloud. Various types of evolutionary algorithms have also been developed in addressing the problems where these contribute to the evolutionary computation field. The algorithms provide solutions for efficient resource management of composite SaaS in Cloud that resulted to a low total cost of ownership for users while guaranteeing the SaaS performance.

A novel representation of bioacoustic events for content-based search in field audio data

Bioacoustic data can provide an important base for environmental monitoring. To explore a large amount of field recordings collected, an automated similarity search algorithm is presented in this paper. A region of an audio defined by frequency and time bounds is provided by a user; the content of the region is used to construct a query. In the retrieving process, our algorithm will automatically scan through recordings to search for similar regions. In detail, we present a feature extraction approach based on the visual content of vocalisations – in this case ridges, and develop a generic regional representation of vocalisations for indexing. Our feature extraction method works best for bird vocalisations showing ridge characteristics. The regional representation method allows the content of an arbitrary region of a continuous recording to be described in a compressed format.

A business engineering approach to client relationship management in a knowledge based firm

This thesis examines Customer Relationship Management and how the capabilities of an organisation to innovate can be enhanced via its implementation in a Knowledge Based Firm. The research identifies current customer knowledge components within an organisation and identifies for future use, CRM components for implementation within a Knowledge Based Firm. Opinions from a panel of experts' are identified for best practice customer relationship strategy, the most important CRM processes and identification of customer knowledge components that will form the basis of implementing a successful CRM to gain a competitive advantage through enhancing the innovative capability for a Knowledge Based Firm.