885 resultados para later life


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This thesis reports the outcomes of an investigation into students’ experience of Problem-based learning (PBL) in virtual space. PBL is increasingly being used in many fields including engineering education. At the same time many engineering education providers are turning to online distance education. Unfortunately there is a dearth of research into what constitutes an effective learning experience for adult learners who undertake PBL instruction through online distance education. Research was therefore focussed on discovering the qualitatively different ways that students experience PBL in virtual space. Data was collected in an electronic environment from a course, which adopted the PBL strategy and was delivered entirely in virtual space. Students in this course were asked to respond to open-ended questions designed to elicit their learning experience in the course. Data was analysed using the phenomenographical approach. This interpretative research method concentrated on mapping the qualitative differences in students’ interpretations of their experience in the course. Five qualitatively different ways of experiencing were discovered: Conception 1: ‘A necessary evil for program progression’; Conception 2: ‘Developing skills to understand, evaluate, and solve technical Engineering and Surveying problems’; Conception 3: ‘Developing skills to work effectively in teams in virtual space’; Conception 4: ‘A unique approach to learning how to learn’; Conception 5: ‘Enhancing personal growth’. Each conception reveals variation in how students attend to learning by PBL in virtual space. Results indicate that the design of students’ online learning experience was responsible for making students aware of deeper ways of experiencing PBL in virtual space. Results also suggest that the quality and quantity of interaction with the team facilitator may have a significant impact on the student experience in virtual PBL courses. The outcomes imply pedagogical strategies can be devised for shifting students’ focus as they engage in the virtual PBL experience to effectively manage the student learning experience and thereby ensure that they gain maximum benefit. The results from this research hold important ramifications for graduates with respect to their ease of transition into professional work as well as their later professional competence in terms of problem solving, ability to transfer basic knowledge to real-life engineering scenarios, ability to adapt to changes and apply knowledge in unusual situations, ability to think critically and creatively, and a commitment to continuous life-long learning and self-improvement.

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Chapter summary • The adolescent and work — Advantages of part-time work — Disadvantages of part-time work — Theory/literature re vocational issues — Influences on vocational choice — How do we prepare young people for thinking about work? • The Education and Training Reforms for the Future (ETRF) in Australia: Learning or earning — What these changes mean for young people — VET (Vocational Education and Training) • Summary • Key points • Further thinking • References Who we are, our self-concept and self-esteem, for many people is tied closely to what we do. Our profession, our employment and our ambitions define us in many ways. In our society we have not yet separated completely the notion of personal worth from social contribution and status. At Australian BBQs, a pretty staple question to ask is ‘So, what do you do?’ when meeting someone new. We are pretty tolerant with a range of responses to that question, but the bottom line is the notion that there ought to be a coherent answer. Adolescents know this, and as they try to define their identity/identities and launch into adulthood they are confronted with the great unknown, the world of work...

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For Bakhtin, it is always important to know from where one speaks. The place from which I speak is that of a person who grew up in Italy during the economic miracle (pre-1968) in a working class family, watching film matinees on television during school holidays. All sort of films and genres were shown: from film noir to westerns, to Jean Renoir's films, German expressionism, Italian neorealism and Italian comedy. Cinema has come to represent over time a sort of memory extension that supplements lived memory of events, and one which, especially, mediates the intersection of many cultural discourses. When later in life I moved to Australia and started teaching in film studies, my choice of a film that was emblematic of neorealism went naturally to Roma città aperta (Open city hereafter) by Roberto Rossellini (1945), and not to Paisan or Sciuscà or Bicycle Thieves. My choice was certainly grounded in my personal memory - especially those aspects transmitted to me by my parents, who lived through the war and maintained that Open City had truly made them cry. With a mother who voted for the Christian Democratic Party and a father who was a unionist, I thought that this was normal in Italian families and society. In the early 1960s, the Resistance still offered a narrative of suffering and redemption, shared by Catholics or Communists. This construction of psychological realism is what I believe Open City continues to offer in time.

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The shortage of donor hearts for patients with end stage heart failure has accelerated the development of ventricular assist devices (VAD) that act as a replacement heart. Mechanical devices involving pulsatile, axial and centrifugal devices have been proposed. Recent clinical developments indicate that centrifugal devices are not only beneficial for bridge to transplantation applications, but may also aid myocardial recovery. The results of a recent study have shown that patients who received a VAD have extended lives and improved quality of life compared to recipients of drug therapy. Unfortunately 25% of these patients develop right heart failure syndrome, sepsis and multi-organ failure. It was reported that 17% of patients initially receiving an LVAD later required a right ventricular assist device (RVAD). Hence, current research focus is in the development of a bi-ventricular assist device (BVAD). Current BVAD technology is either too bulky or necessitates having to implant two pumps working independently. The latter requires two different controllers for each pump leading to the potential complication of uneven flow dynamics and the requirements for a large amount of body space. This paper illustrates the combination of the LVAD and RVAD as one complete device to augment the function of both the left and right cardiac chambers with double impellers. The proposed device has two impellers rotating in counter directions, hence eliminating the necessity of the body muscles and tubing/heart connection to restrain the pump. The device will also have two separate chambers with independent rotating impeller for the left and right chambers. A problem with centrifugal impellers is the fluid stagnation underneath the impeller. This leads to thrombosis and blood clots.This paper presents the design, construction and location of washout hole to prevent thrombus for a Bi-VAD centrifugal pump. Results using CFD will be used to illustrate the superiority of our design concept in terms of preventing thrombus formation and hemolysis.

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Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

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The epilogue pulls together the conceptual and methodological significance of the papers in the special issue exploring childhood and social interaction in everyday life in Sweden, Norway, United States and Australia. In considering the special issue, four domains of childhood are identified and discussed: childhood is a social construct where children learn how to enter into and participate in their social organizations, competency is best understood when communicative practices are examined in situ, children’s talk and interaction show situated culture in action, and childhood consists of shared social orders between children and adults. Emerging analytic interests are proposed, including investigating how children understand locations and place. Finally, the epilogue highlights the core focus of this special issue, which is showing children’s own methods for making sense of their everyday contexts using the interactional and cultural resources they have to hand.

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The construction industry has adapted information technology in its processes in terms of computer aided design and drafting, construction documentation and maintenance. The data generated within the construction industry has become increasingly overwhelming. Data mining is a sophisticated data search capability that uses classification algorithms to discover patterns and correlations within a large volume of data. This paper presents the selection and application of data mining techniques on maintenance data of buildings. The results of applying such techniques and potential benefits of utilising their results to identify useful patterns of knowledge and correlations to support decision making of improving the management of building life cycle are presented and discussed.

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Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

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The building life cycle process is complex and prone to fragmentation as it moves through its various stages. The number of participants, and the diversity, specialisation and isolation both in space and time of their activities, have dramatically increased over time. The data generated within the construction industry has become increasingly overwhelming. Most currently available computer tools for the building industry have offered productivity improvement in the transmission of graphical drawings and textual specifications, without addressing more fundamental changes in building life cycle management. Facility managers and building owners are primarily concerned with highlighting areas of existing or potential maintenance problems in order to be able to improve the building performance, satisfying occupants and minimising turnover especially the operational cost of maintenance. In doing so, they collect large amounts of data that is stored in the building’s maintenance database. The work described in this paper is targeted at adding value to the design and maintenance of buildings by turning maintenance data into information and knowledge. Data mining technology presents an opportunity to increase significantly the rate at which the volumes of data generated through the maintenance process can be turned into useful information. This can be done using classification algorithms to discover patterns and correlations within a large volume of data. This paper presents how and what data mining techniques can be applied on maintenance data of buildings to identify the impediments to better performance of building assets. It demonstrates what sorts of knowledge can be found in maintenance records. The benefits to the construction industry lie in turning passive data in databases into knowledge that can improve the efficiency of the maintenance process and of future designs that incorporate that maintenance knowledge.

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Objective: This paper explores the effects of perceived stage of cancer (PSOC) on carers' anxiety and depression during the patients' final year. Methods: A consecutive sample of patients and carers (N=98) were surveyed at regular intervals regarding PSOC, and anxiety and depression using the Hospital Anxiety and Depression Scale. Means were compared by gender using the Mann-Whitney U-test. The chi-square was used to analyse categorical data. Agreement between carers' and patients' PSOC was estimated using kappa statistics. Correlations between carers' PSOC and their anxiety and depression were calculated using the Spearman's rank correlation. Results: Over time, an increasing proportion of carers reported that the cancer was advanced, culminating at 43% near death. Agreement regarding PSOC was fair (kappa=0.29-0.34) until near death (kappa=0.21). Carers' anxiety increased over the year; depression increased in the final 6 months. Females were more anxious (p=0.049, 6 months; p=0.009, 3 months) than males, and more depressed until 1 month to death. The proportion of carers reporting moderate-severe anxiety almost doubled over the year to 27%, with more females in this category at 6 months (p=0.05). Carers with moderate-severe depression increased from 6 to 15% over the year. Increased PSOC was weakly correlated with increased anxiety and depression. Conclusions: Carers' anxiety exceeded depression in severity during advanced cancer. Females generally experienced greater anxiety and depression. Carers were more realistic than patients regarding the ultimate outcome, which was reflected in their declining mental health, particularly near the end.

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There is much still to learn about how young children’s membership with peers shapes their constructions of moral and social obligations within everyday activities in the school playground. This paper investigates how a small group of girls, aged four to six years, account for their everyday social interactions in the playground. They were video-recorded as they participated in a pretend game of school. Several days later, a video-recorded excerpt of the interaction was shown to them and invited to comment on what was happening in the video. This conversation was audio-recorded. Drawing on a conversation analysis approach, this chapter shows that, despite their discontent and complaining about playing the game of school, the girls’ actions showed their continued orientation to the particular codes of the game, of ‘no going away’ and ‘no telling’. By making relevant these codes, jointly constructed by the girls during the interview, they managed each other’s continued participation within two arenas of action: the pretend, as a player in a pretend game of school; and the real, as a classroom member of a peer group. Through inferences to explicit and implicit codes of conduct, moral obligations were invoked as the girls attempted to socially exclude or build alliances with others, and enforce their own social position. As well, a shared history that the girls re-constructed has moral implications for present and future relationships. The girls oriented to the history as an interactional resource for accounting for their actions in the pretend game. This paper uncovers how children both participate in, and shape, their everyday social worlds through talk and interaction and the consequences a taken-for-granted activity such as playing school has for their moral and social positions in the peer group.

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The report presents a methodology for whole of life cycle cost analysis of alternative treatment options for bridge structures, which require rehabilitation. The methodology has been developed after a review of current methods and establishing that a life cycle analysis based on a probabilistic risk approach has many advantages including the essential ability to consider variability of input parameters. The input parameters for the analysis are identified as initial cost, maintenance, monitoring and repair cost, user cost and failure cost. The methodology utilizes the advanced simulation technique of Monte Carlo simulation to combine a number of probability distributions to establish the distribution of whole of life cycle cost. In performing the simulation, the need for a powerful software package, which would work with spreadsheet program, has been identified. After exploring several products on the market, @RISK software has been selected for the simulation. In conclusion, the report presents a typical decision making scenario considering two alternative treatment options.