A systematic review and meta-analysis : tailoring asthma treatment on eosinophilic markers (exhaled nitric oxide or sputum eosinophils)

Asthma severity and control can be measured both subjectively and objectively. Traditionally asthma treatments have been individualised using symptoms and spirometry/peak flow. Increasingly treatment tailored in accordance with inflammatory markers (sputum eosinophil counts or fractional exhaled nitric oxide (FeNO) data) is advocated as an alternative strategy. The objective of this review was to evaluate the efficacy of tailoring asthma interventions based on inflammatory markers (sputum analysis and FeNO) in comparison with clinical symptoms (with or without spirometry/peak flow) for asthma-related outcomes in children and adults. Cochrane Airways Group Specialised Register of Trials, the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE and reference lists of articles were searched. The last searches were in February 2009. All randomised controlled comparisons of adjustment of asthma treatment based on sputum analysis or FeNO compared with traditional methods (primarily clinical symptoms and spirometry/peak flow) were selected. Results of searches were reviewed against predetermined criteria for inclusion. Relevant studies were selected, assessed and data extracted independently by at least two people. The trial authors were contacted for further information. Data were analysed as 'intervention received' and sensitivity analyses performed. Six (2 adults and 4 children/adolescent) studies utilising FeNO and three adult studies utilising sputum eosinophils were included. These studies had a degree of clinical heterogeneity including definition of asthma exacerbations, duration of study and variations in cut-off levels for percentage of sputum eosinophils and FeNO to alter management in each study. Adults who had treatment adjusted according to sputum eosinophils had a reduced number of exacerbations compared with the control group (52 vs. 77 patients with >=1 exacerbation in the study period; p=0.0006). There was no significant difference in exacerbations between groups for FeNO compared with controls. The daily dose of inhaled corticosteroids at the end of the study was decreased in adults whose treatment was based on FeNO in comparison with the control group (mean difference -450.03 mug, 95% CI -676.73 to -223.34; p<0.0001). However, children who had treatment adjusted according to FeNO had an increase in their mean daily dose of inhaled corticosteroids (mean difference 140.18 mug, 95% CI 28.94 to 251.42; p=0.014). It was concluded that tailoring of asthma treatment based on sputum eosinophils is effective in decreasing asthma exacerbations. However, tailoring of asthma treatment based on FeNO levels has not been shown to be effective in improving asthma outcomes in children and adults. At present, there is insufficient justification to advocate the routine use of either sputum analysis (due to technical expertise required) or FeNO in everyday clinical practice.

Team change climate : a group-level analysis of the relationships among change information and change participation, role stressors, and well-being

We explored whether teams develop shared perceptions regarding the quantity and quality of information and the extent of participation in decision making provided in an environment of continuous change. In addition, we examined whether change climate strength moderated relationships between change climate level and team outcomes. We examined relationships among aggregated change information and change participation and aggregated team outcomes, including two role stressors (i.e., role ambiguity and role overload) and two indicators of well-being (i.e., quality of worklife and distress). Questionnaires were distributed in an Australian law enforcement agency and data were used from 178 teams. Structural equation modelling analyses, controlling for a marker variable, were conducted to examine the main effects of aggregated change information and aggregated change participation on aggregated team outcomes. Results provided support for a model that included method effects due to a marker variable. In this model, change information climate was significantly negatively associated with role ambiguity, role overload, and distress, and significantly positively associated with quality of worklife. Change participation climate was significantly positively associated with quality of worklife. Change climate strength did not moderate relationships among change climate level and team outcomes.

The ranking based constrained document clustering method and its application to social event detection

With the growing size and variety of social media files on the web, it’s becoming critical to efficiently organize them into clusters for further processing. This paper presents a novel scalable constrained document clustering method that harnesses the power of search engines capable of dealing with large text data. Instead of calculating distance between the documents and all of the clusters’ centroids, a neighborhood of best cluster candidates is chosen using a document ranking scheme. To make the method faster and less memory dependable, the in-memory and in-database processing are combined in a semi-incremental manner. This method has been extensively tested in the social event detection application. Empirical analysis shows that the proposed method is efficient both in computation and memory usage while producing notable accuracy.

Summarisation of short-term and long-term videos using texture and colour

We present a novel approach to video summarisation that makes use of a Bag-of-visual-Textures (BoT) approach. Two systems are proposed, one based solely on the BoT approach and another which exploits both colour information and BoT features. On 50 short-term videos from the Open Video Project we show that our BoT and fusion systems both achieve state-of-the-art performance, obtaining an average F-measure of 0.83 and 0.86 respectively, a relative improvement of 9% and 13% when compared to the previous state-of-the-art. When applied to a new underwater surveillance dataset containing 33 long-term videos, the proposed system reduces the amount of footage by a factor of 27, with only minor degradation in the information content. This order of magnitude reduction in video data represents significant savings in terms of time and potential labour cost when manually reviewing such footage.

Dictionary learning and sparse coding on Grassmann manifolds : an extrinsic solution

Recent advances in computer vision and machine learning suggest that a wide range of problems can be addressed more appropriately by considering non-Euclidean geometry. In this paper we explore sparse dictionary learning over the space of linear subspaces, which form Riemannian structures known as Grassmann manifolds. To this end, we propose to embed Grassmann manifolds into the space of symmetric matrices by an isometric mapping, which enables us to devise a closed-form solution for updating a Grassmann dictionary, atom by atom. Furthermore, to handle non-linearity in data, we propose a kernelised version of the dictionary learning algorithm. Experiments on several classification tasks (face recognition, action recognition, dynamic texture classification) show that the proposed approach achieves considerable improvements in discrimination accuracy, in comparison to state-of-the-art methods such as kernelised Affine Hull Method and graph-embedding Grassmann discriminant analysis.

Effects of wireless sensor network uncertainties on output-only modal analysis employing merged data of multiple tests

The use of Wireless Sensor Networks (WSNs) for vibration-based Structural Health Monitoring (SHM) has become a promising approach due to many advantages such as low cost, fast and flexible deployment. However, inherent technical issues such as data asynchronicity and data loss have prevented these distinct systems from being extensively used. Recently, several SHM-oriented WSNs have been proposed and believed to be able to overcome a large number of technical uncertainties. Nevertheless, there is limited research verifying the applicability of those WSNs with respect to demanding SHM applications like modal analysis and damage identification. Based on a brief review, this paper first reveals that Data Synchronization Error (DSE) is the most inherent factor amongst uncertainties of SHM-oriented WSNs. Effects of this factor are then investigated on outcomes and performance of the most robust Output-only Modal Analysis (OMA) techniques when merging data from multiple sensor setups. The two OMA families selected for this investigation are Frequency Domain Decomposition (FDD) and data-driven Stochastic Subspace Identification (SSI-data) due to the fact that they both have been widely applied in the past decade. Accelerations collected by a wired sensory system on a large-scale laboratory bridge model are initially used as benchmark data after being added with a certain level of noise to account for the higher presence of this factor in SHM-oriented WSNs. From this source, a large number of simulations have been made to generate multiple DSE-corrupted datasets to facilitate statistical analyses. The results of this study show the robustness of FDD and the precautions needed for SSI-data family when dealing with DSE at a relaxed level. Finally, the combination of preferred OMA techniques and the use of the channel projection for the time-domain OMA technique to cope with DSE are recommended.

Integration of XML Schemas for the Exchange of FDOT Construction Project Data

An investigation of the construction data management needs of the Florida Department of Transportation (FDOT) with regard to XML standards including development of data dictionary and data mapping. The review of existing XML schemas indicated the need for development of specific XML schemas. XML schemas were developed for all FDOT construction data management processes. Additionally, data entry, approval and data retrieval applications were developed for payroll compliance reporting and pile quantity payment development.

Precise relative clock synchronization for distributed control using TSC registers

Precise clock synchronization is essential in emerging time-critical distributed control systems operating over computer networks where the clock synchronization requirements are mostly focused on relative clock synchronization and high synchronization precision. Existing clock synchronization techniques such as the Network Time Protocol (NTP) and the IEEE 1588 standard can be difficult to apply to such systems because of the highly precise hardware clocks required, due to network congestion caused by a high frequency of synchronization message transmissions, and high overheads. In response, we present a Time Stamp Counter based precise Relative Clock Synchronization Protocol (TSC-RCSP) for distributed control applications operating over local-area networks (LANs). In our protocol a software clock based on the TSC register, counting CPU cycles, is adopted in the time clients and server. TSC-based clocks offer clients a precise, stable and low-cost clock synchronization solution. Experimental results show that clock precision in the order of 10~microseconds can be achieved in small-scale LAN systems. Such clock precision is much higher than that of a processor's Time-Of-Day clock, and is easily sufficient for most distributed real-time control applications over LANs.

Data collection techniques

The objective of this chapter is to provide an overview of traffic data collection that can and should be used for the calibration and validation of traffic simulation models. There are big differences in availability of data from different sources. Some types of data such as loop detector data are widely available and used. Some can be measured with additional effort, for example, travel time data from GPS probe vehicles. Some types such as trajectory data are available only in rare situations such as research projects.

The impact of nurse practitioner services on cost, quality of care, satisfaction and wait times in the emergency department: A systematic review

Aims To provide the best available evidence to determine the impact of nurse practitioner services on cost, quality of care, satisfaction and waiting times in the emergency department for adult patients. Background The delivery of quality care in the emergency department is one of the most important service indicators in health delivery. Increasing service pressures in the emergency department have resulted in the adoption of service innovation models: the most common and rapidly expanding of these is emergency nurse practitioner services. The rapid uptake of emergency nurse practitioner service in Australia has outpaced the capacity to evaluate this service model in terms of outcomes related to safety and quality of patient care. Previous research is now outdated and not commensurate with the changing domain of delivering emergency care with nurse practitioner services. Data A comprehensive search of four electronic databases from 2006-­‐2013 was conducted to identify research evaluating nurse practitioner service impact in the emergency department. English language articles were sought using MEDLINE, CINAHL, Embase and Cochrane and included two previous systematic reviews completed five and seven years ago. Methods A three step approach was used. Following a comprehensive search, two reviewers assessed identified studies against the inclusion criteria. From the original 1013 studies, 14 papers were retained for critical appraisal on methodological quality by two independent reviewers and data extracted using standardised tools. Results Narrative synthesis was conducted to summarise and report the findings as insufficient data was available for meta-­‐analysis of results. This systematic review has shown that emergency nurse practitioner service has a positive impact on quality of care, patient satisfaction and waiting times. There was insufficient evidence to draw conclusions regarding impact on costs. Conclusion Synthesis of the available research attempts to provide an evidence base for emergency nurse practitioner service to guide healthcare leaders, policy makers and clinicians in reforming emergency department service provision. The findings suggest that further quality research is required for comparative measures of clinical and service effectiveness of emergency nurse practitioner service. In the context of increased health service demand and the need to provide timely and effective care to patients, such measures will assist in delivering quality patient care.

Development of gas sensing technology for ground and airborne applications powered by solar energy : methodology and experimental results

Monitoring gases for environmental, industrial and agricultural fields is a demanding task that requires long periods of observation, large quantity of sensors, data management, high temporal and spatial resolution, long term stability, recalibration procedures, computational resources, and energy availability. Wireless Sensor Networks (WSNs) and Unmanned Aerial Vehicles (UAVs) are currently representing the best alternative to monitor large, remote, and difficult access areas, as these technologies have the possibility of carrying specialised gas sensing systems, and offer the possibility of geo-located and time stamp samples. However, these technologies are not fully functional for scientific and commercial applications as their development and availability is limited by a number of factors: the cost of sensors required to cover large areas, their stability over long periods, their power consumption, and the weight of the system to be used on small UAVs. Energy availability is a serious challenge when WSN are deployed in remote areas with difficult access to the grid, while small UAVs are limited by the energy in their reservoir tank or batteries. Another important challenge is the management of data produced by the sensor nodes, requiring large amount of resources to be stored, analysed and displayed after long periods of operation. In response to these challenges, this research proposes the following solutions aiming to improve the availability and development of these technologies for gas sensing monitoring: first, the integration of WSNs and UAVs for environmental gas sensing in order to monitor large volumes at ground and aerial levels with a minimum of sensor nodes for an effective 3D monitoring; second, the use of solar energy as a main power source to allow continuous monitoring; and lastly, the creation of a data management platform to store, analyse and share the information with operators and external users. The principal outcomes of this research are the creation of a gas sensing system suitable for monitoring any kind of gas, which has been installed and tested on CH4 and CO2 in a sensor network (WSN) and on a UAV. The use of the same gas sensing system in a WSN and a UAV reduces significantly the complexity and cost of the application as it allows: a) the standardisation of the signal acquisition and data processing, thereby reducing the required computational resources; b) the standardisation of calibration and operational procedures, reducing systematic errors and complexity; c) the reduction of the weight and energy consumption, leading to an improved power management and weight balance in the case of UAVs; d) the simplification of the sensor node architecture, which is easily replicated in all the nodes. I evaluated two different sensor modules by laboratory, bench, and field tests: a non-dispersive infrared module (NDIR) and a metal-oxide resistive nano-sensor module (MOX nano-sensor). The tests revealed advantages and disadvantages of the two modules when used for static nodes at the ground level and mobile nodes on-board a UAV. Commercial NDIR modules for CO2 have been successfully tested and evaluated in the WSN and on board of the UAV. Their advantage is the precision and stability, but their application is limited to a few gases. The advantages of the MOX nano-sensors are the small size, low weight, low power consumption and their sensitivity to a broad range of gases. However, selectivity is still a concern that needs to be addressed with further studies. An electronic board to interface sensors in a large range of resistivity was successfully designed, created and adapted to operate on ground nodes and on-board UAV. The WSN and UAV created were powered with solar energy in order to facilitate outdoor deployment, data collection and continuous monitoring over large and remote volumes. The gas sensing, solar power, transmission and data management systems of the WSN and UAV were fully evaluated by laboratory, bench and field testing. The methodology created to design, developed, integrate and test these systems was extensively described and experimentally validated. The sampling and transmission capabilities of the WSN and UAV were successfully tested in an emulated mission involving the detection and measurement of CO2 concentrations in a field coming from a contaminant source; the data collected during the mission was transmitted in real time to a central node for data analysis and 3D mapping of the target gas. The major outcome of this research is the accomplishment of the first flight mission, never reported before in the literature, of a solar powered UAV equipped with a CO2 sensing system in conjunction with a network of ground sensor nodes for an effective 3D monitoring of the target gas. A data management platform was created using an external internet server, which manages, stores, and shares the data collected in two web pages, showing statistics and static graph images for internal and external users as requested. The system was bench tested with real data produced by the sensor nodes and the architecture of the platform was widely described and illustrated in order to provide guidance and support on how to replicate the system. In conclusion, the overall results of the project provide guidance on how to create a gas sensing system integrating WSNs and UAVs, how to power the system with solar energy and manage the data produced by the sensor nodes. This system can be used in a wide range of outdoor applications, especially in agriculture, bushfires, mining studies, zoology, and botanical studies opening the way to an ubiquitous low cost environmental monitoring, which may help to decrease our carbon footprint and to improve the health of the planet.

Ageing in Fiji : how older teachers perceive ageing and their lives

Worldwide the population is ageing and data concerning how people want to age actively is limited. The paper is a description of an inductive interpretive-descriptive study of how a sample of older retired teachers in Fiji viewed ageing and their lives as older people. The objectives were to determine and describe perceptions of ageing held by a sample of retired teachers. The methodology consisted of responses to an open ended questionnaire similar to a phenomenographic approach and the analysis was interpretive – descriptive. A purposive sample of 30 retired teachers between the ages of 55 and 60 responded to the questionnaire. The results indicate that most of the respondents were positive about lifelong learning and in particular learning new things; that they were involved in a range of post retirement activities for personal and financial reasons; that there were some barriers and facilitators to their activities; that they generally accepted ageing and being older; and that more should be done by Government and other agencies to provide for a better life for older people in Fiji. These results should be considered in future planning for ageing populations in Fiji, the Pacific region and in other developing countries.

A random key genetic algorithm for live migration of multiple virtual machines in data centers

Live migration of multiple Virtual Machines (VMs) has become an integral management activity in data centers for power saving, load balancing and system maintenance. While state-of-the-art live migration techniques focus on the improvement of migration performance of an independent single VM, only a little has been investigated to the case of live migration of multiple interacting VMs. Live migration is mostly influenced by the network bandwidth and arbitrarily migrating a VM which has data inter-dependencies with other VMs may increase the bandwidth consumption and adversely affect the performances of subsequent migrations. In this paper, we propose a Random Key Genetic Algorithm (RKGA) that efficiently schedules the migration of a given set of VMs accounting both inter-VM dependency and data center communication network. The experimental results show that the RKGA can schedule the migration of multiple VMs with significantly shorter total migration time and total downtime compared to a heuristic algorithm.

Molecular phylogeny, biogeography, and habitat preference evolution of marsupials

Marsupials exhibit great diversity in ecology and morphology. However, compared to their sister group, the placental mammals, our understanding of many aspects of marsupial evolution remains limited. We use 101 mitochondrial genomes and data from 26 nuclear loci to reconstruct a dated phylogeny including 97% of extant genera and 58% of modern marsupial species. This tree allows us to analyze the evolution of habitat preference and geographic distributions of marsupial species through time. We found a pattern of mesic-adapted lineages evolving to use more arid and open habitats, which is broadly consistent with regional climate and environmental change. However, contrary to the general trend, several lineages subsequently appear to have reverted from drier to more mesic habitats. Biogeographic reconstructions suggest that current views on the connectivity between Australia and New Guinea/Wallacea during the Miocene and Pliocene need to be revised. The antiquity of several endemic New Guinean clades strongly suggests a substantially older period of connection stretching back to the Middle Miocene, and implies that New Guinea was colonized by multiple clades almost immediately after its principal formation.

An efficient and robust system for multi-person event detection in real world indoor surveillance scenes

Due to the popularity of security cameras in public places, it is of interest to design an intelligent system that can efficiently detect events automatically. This paper proposes a novel algorithm for multi-person event detection. To ensure greater than real-time performance, features are extracted directly from compressed MPEG video. A novel histogram-based feature descriptor that captures the angles between extracted particle trajectories is proposed, which allows us to capture motion patterns of multi-person events in the video. To alleviate the need for fine-grained annotation, we propose the use of Labelled Latent Dirichlet Allocation, a “weakly supervised” method that allows the use of coarse temporal annotations which are much simpler to obtain. This novel system is able to run at approximately ten times real-time, while preserving state-of-theart detection performance for multi-person events on a 100-hour real-world surveillance dataset (TRECVid SED).