On Queueinginventory Models – product form solution; reservation, Cancellation and common life time

Queueing theory is the mathematical study of ‘queue’ or ‘waiting lines’ where an item from inventory is provided to the customer on completion of service. A typical queueing system consists of a queue and a server. Customers arrive in the system from outside and join the queue in a certain way. The server picks up customers and serves them according to certain service discipline. Customers leave the system immediately after their service is completed. For queueing systems, queue length, waiting time and busy period are of primary interest to applications. The theory permits the derivation and calculation of several performance measures including the average waiting time in the queue or the system, mean queue length, traffic intensity, the expected number waiting or receiving service, mean busy period, distribution of queue length, and the probability of encountering the system in certain states, such as empty, full, having an available server or having to wait a certain time to be served.

Exploring the intersections of creative and academic life among Australian academic creative writing practitioners

Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015 : a systematic analysis for the Global Burden of Disease Study 2015

Global, regional, and national disability-adjusted life-years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE), 1990–2015 : a systematic analysis for the Global Burden of Disease Study 2015

Women and work-life balance: a case of austerity and authenticity

Recent evidence suggests that support for gender equality appears to be declining amid concern that women who play a full role in the work force do so at the expense of family life. Although this has been exacerbated by the economic downturn there were pre-recessionary signs of concerns that the welfare of children was being compromised when women find themselves with the double burden of employment and family care. These work-life balance issues are examined from the point of view of looking at how this characterised for women as a problem of authentic self-realisation in terms of working towards and achieving an optimum balance. This kind of dichotomisation allows for different aspects of the self to be pitted against each other: the professional career-orientated aspect and the personal relationship aspect. The paper argues that work-life balance discourse is counter-productive in that it reinforces the inequalities for women that it seeks to redress.

Viral Bodies: Uncontrollable Blackness in Popular Culture and Everyday Life

Viral Bodies: Uncontrollable Blackness in Popular Culture and Everyday Life maps rapidly circulated performances of Blackness across visual media that collapse Black bodies into ubiquitous “things.” Throughout my dissertation, I use viral performance to describe the uncontrollable discursive circulation of bodies, their behaviors, and the ideas around them. In particular, viral performance is employed to describe the complicated ways that (mis)understandings of Black bodies spread and are often transformed into common-sense beliefs. As viral performances, Black bodies are often made more visible, while simultaneously becoming more opaque. This dissertation examines the recurrence of viral performances of Blackness in viral videos online, film, and photography/images. I argue that viral performances make products that reinscribe stereotypical notions of Blackness while also generating paths of alterity—which contradict the normalized clichés and provide desirable possibilities for Black performance. Viral Bodies forges a new dialogue between visual and aural technologies, performance, and larger historic discourses that script Black bodies as visually (and sonically) deviant subjects. I am interested in how technologies complicate the re-presentation of images, ideas, and ideologies—producing a necessity for new decipherings of performances of Blackness in popular culture and everyday life.

The pre-reproductive period in a tropical bird: parental care, dispersal, survival, and avian life histories

The period between offspring birth and recruitment into the breeding population is considered one of the least understood components of animal life histories. Yet, examining this period is essential for studies of parental care, dispersal, demography, and life histories. Studies of the pre-reproductive period are particularly few in tropical regions, where the organization of life histories are predicted to differ compared to northern hemisphere species. For my dissertation I used radio-telemetry, mark-resighting, and field observations to study the pre-reproductive period in a Neotropical bird, the western slaty-antshrike (Thamnophilus atrinucha), in Panama. First, I found that parental care after offspring left the nest (the post-fledging period) was greater than care during the nestling period. Prolonged care resulted in a clear trade-off for parents as they did not nest again until fledglings from the first brood were independent. Parents fed offspring for a prolonged duration during the post-fledging period and higher post-fledging survival was observed compared to many northern hemisphere species. Second, I observed that offspring that remained with parents for longer periods on the natal territory had higher survival both while on the natal territory and after dispersal compared to those dispersing earlier. Parental aggression towards offspring increased with offspring age and offspring dispersed earlier when parents renested. Contrary to other family living species, only a small proportion of antshrike offspring remained on the natal territory until the following year and all dispersed to float. Floating is when juveniles wander within other breeding pairs’ territories. These results suggest that the benefits of delayed dispersal declined with offspring age and with renesting by parents. Third, I observed that survival during the dependent period and first year was greater in slaty antshrikes compared to that of northern hemisphere species. Pre-reproductive survival relative to adult survival was equal or greater than that observed in northern hemisphere species. The date offspring left the nest, mass, and age at dispersal influenced offspring survival, whereas offspring sex and year did not. Relatively high survival during the pre-reproductive period coupled with comparatively low annual productivity clarifies how many tropical species achieve replacement. High juvenile survival appears to obtain from extended post-fledging parental care, delayed dispersal, low costs of dispersal, and a less seasonal environment. Lastly, I experimentally manipulated begging at the nest to examine changes in parental behavior. Under elevated begging, parents increased provisioning rates and reduced the time between arrival to the nest and feeding of nestlings, potentially to reduce begging sounds. Furthermore, parents switched to preferentially feed the closest offspring during the begging treatment. This suggests parents either allowed sibling competition to influence feeding decisions, or feeding the closer nestling increased the efficiency of provisioning. In summary, I found that slaty antshrikes have delayed age at reproduction, higher post-fledging and first year survival, extended post-fledging parental care, equal or greater pre-reproductive survival relative to adult survival, and delayed dispersal compared to many northern hemisphere passerines. These results suggest that this tropical species has a strategy of high investment into few offspring. Furthermore, reproductive effort is equal or greater at least in slaty antshrikes compared to northern hemisphere species, suggesting that the latitudinal gradient in clutch size is not explained by a gradient in reproductive effort.

The Performance and Service Life Prediction of High Performance Concrete in Sulfate and Acidic Environments

Concrete substructures are often subjected to environmental deterioration, such as sulfate and acid attack, which leads to severe damage and causes structure degradation or even failure. In order to improve the durability of concrete, the High Performance Concrete (HPC) has become widely used by partially replacing cement with pozzolanic materials. However, HPC degradation mechanisms in sulfate and acidic environments are not completely understood. It is therefore important to evaluate the performance of the HPC in such conditions and predict concrete service life by establishing degradation models. This study began with a review of available environmental data in the State of Florida. A total of seven bridges have been inspected. Concrete cores were taken from these bridge piles and were subjected for microstructural analysis using Scanning Electron Microscope (SEM). Ettringite is found to be the products of sulfate attack in sulfate and acidic condition. In order to quantitatively analyze concrete deterioration level, an image processing program is designed using Matlab to obtain quantitative data. Crack percentage (Acrack/Asurface) is used to evaluate concrete deterioration. Thereafter, correlation analysis was performed to find the correlation between five related variables and concrete deterioration. Environmental sulfate concentration and bridge age were found to be positively correlated, while environmental pH level was found to be negatively correlated. Besides environmental conditions, concrete property factor was also included in the equation. It was derived from laboratory testing data. Experimental tests were carried out implementing accelerated expansion test under controlled environment. Specimens of eight different mix designs were prepared. The effect of pozzolanic replacement rate was taken into consideration in the empirical equation. And the empirical equation was validated with existing bridges. Results show that the proposed equations compared well with field test results with a maximum deviation of ± 20%. Two examples showing how to use the proposed equations are provided to guide the practical implementation. In conclusion, the proposed approach of relating microcracks to deterioration is a better method than existing diffusion and sorption models since sulfate attack cause cracking in concrete. Imaging technique provided in this study can also be used to quantitatively analyze concrete samples.

Parental unemployment and youth life satisfaction: The moderating roles of satisfaction with family life

Abstract While Europe is slowly recovering from the economic recession, its effects on labour markets are still visible. The number of jobless families has increased and previous research has shown that unemployment can affect the wellbeing of both parents and their children. In this study we explored the links between parental unemployment and youth life satisfaction by considering the potential moderating roles played by satisfaction with family life and perceived family wealth. We used descriptive statistics, correlations, simple moderation and moderated moderation models of regression on data from a representative sample of 3937 Portuguese students (Mage = 13.9 years; SD ± 1.7; 48 % boys). Results showed that the negative effects of parental unemployment on youth life satisfaction were moderated by youth perceived satisfaction with family life but not by perceived wealth. This suggested that during family unemployment, young people satisfied with their family life are less vulnerable to the negative effects of parental unemployment on their life satisfaction. The relationship between parental unemployment and youth well-being requires further research, especially during periods of labour market crisis.

Life cycle modelling and design knowledge development in 3D virtual environments : final report

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.

Life Cycle Modelling and Design Knowledge Development in 3D Virtual Environments

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.

Discovering, applying and integrating self-knowledge : a grounded theory study of learning in life coaching

Professional coaching is a rapidly expanding field with interdisciplinary roots and broad application. However, despite abundant prescriptive literature, research into the process of coaching, and especially life coaching, is minimal. Similarly, although learning is inherently recognised in the process of coaching, and coaching is increasingly being recognised as a means of enhancing teaching and learning, the process of learning in coaching is little understood, and learning theory makes up only a small part of the evidence-based coaching literature. In this grounded theory study of life coaches and their clients, the process of learning in life coaching across a range of coaching models is examined and explained. The findings demonstrate how learning in life coaching emerged as a process of discovering, applying and integrating self-knowledge, which culminated in the development of self. This process occurred through eight key coaching processes shared between coaches and clients and combined a multitude of learning theory.