568 resultados para Multiple solutions


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The application of layered double hydroxides (LDHs) and thermally activated LDHs for the removal of various fluorine (F-, BF-4), chlorine (Cl-,ClO-4), bromine (Br-, BrO-3) and iodine (I-, IO-3) species from aqueous solutions has been reviewed in this article. LDHs and thermally activated LDHs were able to significantly reduce the concentration of selected anions in laboratory scale experiments. The M2+:M3+ cation ratio of the LDH adsorbent was an important factor which influenced anion uptake. Though LDHs were able to remove some target anion species through anion exchange and surface adsorption thermal activation and reformation generally produced better results. The presence of competing anions including carbonate, phosphate and sulphate had a significant impact on uptake of the target anion as LDHs typically exhibit lower affinity towards monovalent anions compared to anions with multiple charges. The removal of fluoride and perchlorate from aqueous solution by a continuous flow system utilising fixed bed columns packed with LDH adsorbents has also been investigated. The adsorption capacity of the columns at breakpoint was heavily dependent on the flow rate and lower than result reported for the corresponding batch methods. There is still considerable scope for future research on numerous topics summarised in this article.

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A novel composite material based on deposition of nanosized zero-valent iron (nZVI) particles on acid-leached diatomite was synthesised for the removal of a chlorinated contaminant in water. The nZVI /diatomite composites were characterized by X-ray diffraction, scanning electron microscopy, elemental analysis, transmission electron microscopy and X-ray photoelectron spectroscopy. Compared with the pure nZVI particles, better dispersion of nZVI particles on the surface or inside the pores of diatom shells was observed. The herbicide simazine was selected as the model chlorinated contaminant and the removal efficiency by nZVI /diatomite composite was compared with that of the pristine nZVI and commercial iron powder. It was found that the diatomite supported nZVI composite material prepared by centrifugation exhibits relatively better efficient activity in decomposition of simazine than commercial Fe, lab synthesized nZVI and composite material prepared via rotary evaporation, and the optimum experimental conditions were obtained based on a series of batch experiments. This study on immobilizing nZVI particles onto diatomite opens a new avenue for the practical application of nZVI and the diatomite-supported nanosized zero-valent iron composite materials have potential applications in environmental remediation.

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OBJECTIVE To compare different reliability coefficients (exact agreement, and variations of the kappa (generalised, Cohen's and Prevalence Adjusted and Biased Adjusted (PABAK))) for four physiotherapists conducting visual assessments of scapulae. DESIGN Inter-therapist reliability study. SETTING Research laboratory. PARTICIPANTS 30 individuals with no history of neck or shoulder pain were recruited with no obvious significant postural abnormalities. MAIN OUTCOME MEASURES Ratings of scapular posture were recorded in multiple biomechanical planes under four test conditions (at rest, and while under three isometric conditions) by four physiotherapists. RESULTS The magnitude of discrepancy between the two therapist pairs was 0.04 to 0.76 for Cohen's kappa, and 0.00 to 0.86 for PABAK. In comparison, the generalised kappa provided a score between the two paired kappa coefficients. The difference between mean generalised kappa coefficients and mean Cohen's kappa (0.02) and between mean generalised kappa and PABAK (0.02) were negligible, but the magnitude of difference between the generalised kappa and paired kappa within each plane and condition was substantial; 0.02 to 0.57 for Cohen's kappa and 0.02 to 0.63 for PABAK, respectively. CONCLUSIONS Calculating coefficients for therapist pairs alone may result in inconsistent findings. In contrast, the generalised kappa provided a coefficient close to the mean of the paired kappa coefficients. These findings support an assertion that generalised kappa may lead to a better representation of reliability between three or more raters and that reliability studies only calculating agreement between two raters should be interpreted with caution. However, generalised kappa may mask more extreme cases of agreement (or disagreement) that paired comparisons may reveal.

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For the last decade, one question has haunted me: what helps people to cope with large-scale organisational change in their workplace? This study explores the construct of personal change resilience, and its potential for identifying solutions to the problems of change fatigue and change resistance. The thesis has emerged from the fields of change management, leadership, training, mentoring, evaluation, management and trust within the context of higher education in Australia at the beginning of the twenty-first century. In this thesis I present a theoretical model of the factors to consider in increasing peoples’ personal change resilience as they navigate large-scale organisational change at work, thereby closing a gap in the literature on the construct of change resilience. The model presented is based on both the literature in the realms of business and education, and on the findings of the research. In this thesis, an autoethnographic case study of two Australian university projects is presented as one narrative, resulting in a methodological step forward in the use of multiple research participants’ stories in the development of a single narrative. The findings describe the experiences of workers in higher education and emphasise the importance of considerate management in the achievement of positive experiences of organisational change. This research makes a significant contribution to new knowledge in three ways. First, it closes a gap in the literature in the realm of change management around personal change resilience as a solution to the problem of change fatigue by presenting models of both change failure and personal change resilience. Second, it is methodologically innovative in the use of personae to tell the stories of multiple participants in one coherent tale presented as a work of ethnographic fiction seen through an autoethnographic lens. By doing so, it develops a methodology for giving a voice to those to whom change is done in the workplace. Third, it provides a perspective on organisational change management from the view of the actual workers affected by change, thereby adding to the literature that currently exists, which is based on the views of those with responsibility for leading or managing change rather than those it affects. This thesis is intended as a practical starting point for conversations by actual change managers in higher education, and it is written in such a way as to help them see how theory can be applied in real life, and how empowering and enabling the actual working staff members, and engaging with them in a considerate way before, during and even after the change process, can help to make them resilient enough to cope with the change, rather than leaving them burned out or disengaged and no longer a well-functioning member of the institution. This thesis shows how considerately managed large-scale organisational change can result in positive outcomes for both the organisation and the individuals who work in it.

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A major challenge for robot localization and mapping systems is maintaining reliable operation in a changing environment. Vision-based systems in particular are susceptible to changes in illumination and weather, and the same location at another time of day may appear radically different to a system using a feature-based visual localization system. One approach for mapping changing environments is to create and maintain maps that contain multiple representations of each physical location in a topological framework or manifold. However, this requires the system to be able to correctly link two or more appearance representations to the same spatial location, even though the representations may appear quite dissimilar. This paper proposes a method of linking visual representations from the same location without requiring a visual match, thereby allowing vision-based localization systems to create multiple appearance representations of physical locations. The most likely position on the robot path is determined using particle filter methods based on dead reckoning data and recent visual loop closures. In order to avoid erroneous loop closures, the odometry-based inferences are only accepted when the inferred path's end point is confirmed as correct by the visual matching system. Algorithm performance is demonstrated using an indoor robot dataset and a large outdoor camera dataset.

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Welcome to the Teacher evidence matrix. This matrix is designed for highly qualified discipline experts to evaluate their teaching in a systematic manner. The primary purpose of the Teacher evidence matrix is to provide a tool that an academic staff member at university can annually review their teaching. The annual review will result in you being ready for performance, planning and review; promotion; awards; or employment application. This tool is designed for individual use and will lead to an action plan for implementation.

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This paper presents a new multi-scale place recognition system inspired by the recent discovery of overlapping, multi-scale spatial maps stored in the rodent brain. By training a set of Support Vector Machines to recognize places at varying levels of spatial specificity, we are able to validate spatially specific place recognition hypotheses against broader place recognition hypotheses without sacrificing localization accuracy. We evaluate the system in a range of experiments using cameras mounted on a motorbike and a human in two different environments. At 100% precision, the multiscale approach results in a 56% average improvement in recall rate across both datasets. We analyse the results and then discuss future work that may lead to improvements in both robotic mapping and our understanding of sensory processing and encoding in the mammalian brain.

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Pretreatments of sugarcane bagasse by three high boiling-point polyol solutions were compared in acid-catalysed processes. Pretreatments by ethylene glycol (EG) and propylene glycol solutions containing 1.2 % H2SO4 and 10 % water at 130 °C for 30 min removed 89 % lignin from bagasse resulting in a glucan digestibility of 95 % with a cellulase loading of ~20 FPU/g glucan. Pretreatment by glycerol solution under the same conditions removed 57 % lignin with a glucan digestibility of 77 %. Further investigations with EG solutions showed that increases in acid content, pretreatment temperature and time, and decrease in water content improved pretreatment effectiveness. A good linear correlation of glucan digestibility with delignification was observed with R2 = 0.984. Bagasse samples pretreated with EG solutions were characterised by scanning electron microscopy, Fourier transform infrared spectroscopy and X-ray diffraction, which confirmed that improved glucan enzymatic digestibility is mainly due to delignification and defibrillation of bagasse. Pretreatment by acidified EG solutions likely led to the formation of EG-glycosides. Up to 36 % of the total lignin was recovered from pretreatment hydrolysate, which may improve the pretreatment efficiency of recycled EG solution.

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A new community and communication type of social networks - online dating - are gaining momentum. With many people joining in the dating network, users become overwhelmed by choices for an ideal partner. A solution to this problem is providing users with partners recommendation based on their interests and activities. Traditional recommendation methods ignore the users’ needs and provide recommendations equally to all users. In this paper, we propose a recommendation approach that employs different recommendation strategies to different groups of members. A segmentation method using the Gaussian Mixture Model (GMM) is proposed to customize users’ needs. Then a targeted recommendation strategy is applied to each identified segment. Empirical results show that the proposed approach outperforms several existing recommendation methods.

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Big Data presents many challenges related to volume, whether one is interested in studying past datasets or, even more problematically, attempting to work with live streams of data. The most obvious challenge, in a ‘noisy’ environment such as contemporary social media, is to collect the pertinent information; be that information for a specific study, tweets which can inform emergency services or other responders to an ongoing crisis, or give an advantage to those involved in prediction markets. Often, such a process is iterative, with keywords and hashtags changing with the passage of time, and both collection and analytic methodologies need to be continually adapted to respond to this changing information. While many of the data sets collected and analyzed are preformed, that is they are built around a particular keyword, hashtag, or set of authors, they still contain a large volume of information, much of which is unnecessary for the current purpose and/or potentially useful for future projects. Accordingly, this panel considers methods for separating and combining data to optimize big data research and report findings to stakeholders. The first paper considers possible coding mechanisms for incoming tweets during a crisis, taking a large stream of incoming tweets and selecting which of those need to be immediately placed in front of responders, for manual filtering and possible action. The paper suggests two solutions for this, content analysis and user profiling. In the former case, aspects of the tweet are assigned a score to assess its likely relationship to the topic at hand, and the urgency of the information, whilst the latter attempts to identify those users who are either serving as amplifiers of information or are known as an authoritative source. Through these techniques, the information contained in a large dataset could be filtered down to match the expected capacity of emergency responders, and knowledge as to the core keywords or hashtags relating to the current event is constantly refined for future data collection. The second paper is also concerned with identifying significant tweets, but in this case tweets relevant to particular prediction market; tennis betting. As increasing numbers of professional sports men and women create Twitter accounts to communicate with their fans, information is being shared regarding injuries, form and emotions which have the potential to impact on future results. As has already been demonstrated with leading US sports, such information is extremely valuable. Tennis, as with American Football (NFL) and Baseball (MLB) has paid subscription services which manually filter incoming news sources, including tweets, for information valuable to gamblers, gambling operators, and fantasy sports players. However, whilst such services are still niche operations, much of the value of information is lost by the time it reaches one of these services. The paper thus considers how information could be filtered from twitter user lists and hash tag or keyword monitoring, assessing the value of the source, information, and the prediction markets to which it may relate. The third paper examines methods for collecting Twitter data and following changes in an ongoing, dynamic social movement, such as the Occupy Wall Street movement. It involves the development of technical infrastructure to collect and make the tweets available for exploration and analysis. A strategy to respond to changes in the social movement is also required or the resulting tweets will only reflect the discussions and strategies the movement used at the time the keyword list is created — in a way, keyword creation is part strategy and part art. In this paper we describe strategies for the creation of a social media archive, specifically tweets related to the Occupy Wall Street movement, and methods for continuing to adapt data collection strategies as the movement’s presence in Twitter changes over time. We also discuss the opportunities and methods to extract data smaller slices of data from an archive of social media data to support a multitude of research projects in multiple fields of study. The common theme amongst these papers is that of constructing a data set, filtering it for a specific purpose, and then using the resulting information to aid in future data collection. The intention is that through the papers presented, and subsequent discussion, the panel will inform the wider research community not only on the objectives and limitations of data collection, live analytics, and filtering, but also on current and in-development methodologies that could be adopted by those working with such datasets, and how such approaches could be customized depending on the project stakeholders.

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The existence of travelling wave solutions to a haptotaxis dominated model is analysed. A version of this model has been derived in Perumpanani et al. (1999) to describe tumour invasion, where diffusion is neglected as it is assumed to play only a small role in the cell migration. By instead allowing diffusion to be small, we reformulate the model as a singular perturbation problem, which can then be analysed using geometric singular perturbation theory. We prove the existence of three types of physically realistic travelling wave solutions in the case of small diffusion. These solutions reduce to the no diffusion solutions in the singular limit as diffusion as is taken to zero. A fourth travelling wave solution is also shown to exist, but that is physically unrealistic as it has a component with negative cell population. The numerical stability, in particular the wavespeed of the travelling wave solutions is also discussed.

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We study a version of the Keller–Segel model for bacterial chemotaxis, for which exact travelling wave solutions are explicitly known in the zero attractant diffusion limit. Using geometric singular perturbation theory, we construct travelling wave solutions in the small diffusion case that converge to these exact solutions in the singular limit.

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The overall aim of this research project was to provide a broader range of value propositions (beyond upfront traditional construction costs) that could transform both the demand side and supply side of the housing industry. The project involved gathering information about how building information is created, used and communicated and classifying building information, leading to the formation of an Information Flow Chart and Stakeholder Relationship Map. These were then tested via broad housing industry focus groups and surveys. The project revealed four key relationships that appear to operate in isolation to the whole housing sector and may have significant impact on the sustainability outcomes and life cycle costs of dwellings over their life cycle. It also found that although a lot of information about individual dwellings does already exist, this information is not coordinated or inventoried in any systematic manner and that national building information files of building passports would present value to a wide range of stakeholders.

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This thesis was the first to define individual lava flow chemical variation and a detailed definition of the Kalkarindji Continental Flood Basalt Province, a lesser known province of the Phanerozoic eon. This thesis conducted an intensive field study that yielded numerous samples for petrography and chemical analyses as well as the generation of a detailed map of a portion of the Kalkarindji province.

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Live migration of multiple Virtual Machines (VMs) has become an indispensible management activity in datacenters for application performance, load balancing, server consolidation. While state-of-the-art live VM migration strategies focus on the improvement of the migration performance of a single VM, little attention has been given to the case of multiple VMs migration. Moreover, existing works on live VM migration ignore the inter-VM dependencies, and underlying network topology and its bandwidth. Different sequences of migration and different allocations of bandwidth result in different total migration times and total migration downtimes. This paper concentrates on developing a multiple VMs migration scheduling algorithm such that the performance of migration is maximized. We evaluate our proposed algorithm through simulation. The simulation results show that our proposed algorithm can migrate multiple VMs on any datacenter with minimum total migration time and total migration downtime.