944 resultados para The job network


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The 21st century has seen renewed interest in activism, community development and social change globally (Kenny 2006). This paper outlines the educational significance of the learning practices of activists as they engage within and against the state. In an era of adult education which emphasises lifelong learning and learning in the workplace, this article explores the holistic practices of activists as they learn from one another in a social context or "on the job". Adult activists act with agency, their learning is purposive; it is resolute and they are there and act for a reason. This learning is not only cognitive but also embodied; it is learning often associated with the emotions of passion, anger, desire and a commitment to social change. Drawing on current research in Australia, attention is given to an important but at times forgotten epistemology of adult learning.

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This paper proposes an efficient solution algorithm for realistic multi-objective median shortest path problems in the design of urban transportation networks. The proposed problem formulation and solution algorithm to median shortest path problem is based on three realistic objectives via route cost or investment cost, overall travel time of the entire network and total toll revenue. The proposed solution approach to the problem is based on the heuristic labeling and exhaustive search technique in criteria space and solution space of the algorithm respectively. The first labels each node in terms of route cost and deletes cyclic and infeasible paths in criteria space imposing cyclic break and route cost constraint respectively. The latter deletes dominated paths in terms of objectives vector in solution space in order to identify a set of Pareto optimal paths. The approach, thus, proposes a non-inferior solution set of Pareto optimal paths based on non-dominated objective vector and leaves the ultimate decision to decision-makers for purpose specific final decision during applications. A numerical experiment is conducted to test the proposed algorithm using artificial transportation network. Sensitivity analyses have shown that the proposed algorithm is advantageous and efficient over existing algorithms to find a set of Pareto optimal paths to median shortest paths problems.

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Traffic classification technique is an essential tool for network and system security in the complex environments such as cloud computing based environment. The state-of-the-art traffic classification methods aim to take the advantages of flow statistical features and machine learning techniques, however the classification performance is severely affected by limited supervised information and unknown applications. To achieve effective network traffic classification, we propose a new method to tackle the problem of unknown applications in the crucial situation of a small supervised training set. The proposed method possesses the superior capability of detecting unknown flows generated by unknown applications and utilizing the correlation information among real-world network traffic to boost the classification performance. A theoretical analysis is provided to confirm performance benefit of the proposed method. Moreover, the comprehensive performance evaluation conducted on two real-world network traffic datasets shows that the proposed scheme outperforms the existing methods in the critical network environment.

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 The International Network for Food and Obesity/non-communicable diseases Research, Monitoring and Action Support (INFORMAS) proposes to collect performance indicators on food policies, actions and environments related to obesity and non-communicable diseases. This paper reviews existing communications strategies used for performance indicators and proposes the approach to be taken for INFORMAS. Twenty-seven scoring and rating tools were identified in various fields of public health including alcohol, tobacco, physical activity, infant feeding and food environments. These were compared based on the types of indicators used and how they were quantified, scoring methods, presentation and the communication and reporting strategies used. There are several implications of these analyses for INFORMAS: the ratings/benchmarking approach is very commonly used, presumably because it is an effective way to communicate progress and stimulate action, although this has not been formally evaluated; the tools used must be trustworthy, pragmatic and policy-relevant; multiple channels of communication will be needed; communications need to be tailored and targeted to decision-makers; data and methods should be freely accessible. The proposed communications strategy for INFORMAS has been built around these lessons to ensure that INFORMAS's outputs have the greatest chance of being used to improve food environments.

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Climate change is perhaps the most pressing and urgent environmental issue facing the world today. However our ability to predict and quantify the consequences of this change is severely limited by the paucity of in situ oceanographic measurements. Marine animals equipped with sophisticated oceanographic data loggers to study their behavior offer one solution to this problem because marine animals range widely across the world’s ocean basins and visit remote and often inaccessible locations. However, unlike the information being collected from conventional oceanographic sensing equipment, which has been validated, the data collected from instruments deployed on marine animals over long periods has not. This is the first long-term study to validate in situ oceanographic data collected by animal oceanographers. We compared the ocean temperatures collected by leatherback turtles (Dermochelys coriacea) in the Atlantic Ocean with the ARGO network of ocean floats and could find no systematic errors that could be ascribed to sensor instability. Animal-borne sensors allowed water temperature to be monitored across a range of depths, over entire ocean basins, and, importantly, over long periods and so will play a key role in assessing global climate change through improved monitoring of global temperatures. This finding is especially pertinent given recent international calls for the development and implementation of a comprehensive Earth observation system (see http://iwgeo.ssc.nasa.gov/documents.asp?s=review) that includes the use of novel techniques for monitoring and understanding ocean and climate interactions to address strategic environmental and societal needs.

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Fire and rescue staff routinely endure significant psychological and environmental stress exposure on the job. While much has been done to improve understanding of the physiological effects of exposure to these conditions, little has been done to quantify the inflammatory stress response that firefighters are exposed to during wildfire suppression. Therefore the aim of the present study was to explore whether firefighters experienced a change in inflammatory markers following one day, and across two days of wildfire suppression tasks. Twelve male fire-fighters participated in two consecutive days of live-fire prescribed burn operations in Ngarkat National Park, South Australia. Typical work tasks included lighting burns, patrolling containment lines, supressing spot fires, and operating vehicles. A number of the inflammatory markers changed significantly across the course of a shift and several presented with an attenuated response across the second day. This finding implies that there was a compounding effect of repeated exposure to these stressors which could have considerable implications for managing fire-fighters health and wellbeing over a multi-day campaign. Further research is required to see which fire ground stressor, or combination of stressors is causing these changes in the inflammatory markers across consecutive work shifts.

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To protect from privacy disclosure, the social network graph is modified in order to hide the information that potentially be used to disclose person's identity. However, when the social network graph is changed, it is a great challenge to balance between the privacy gained and the loss of data utility. In this paper, we address this problem. We propose a new graph topological-based metric to improve utility preservation in social network graph anonymization. We compare the proposed approach with the amount-of-edge-change metric that popularly used in most of previous works. Experimental evaluation shows that our approach generates anonymized social network with improved utility preservation.

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There is growing recognition of the important role of mental health in the workforce and in the workplace. At the same time, there has been a rapid growth of studies linking job stress and other psychosocial working conditions to common mental disorders, and a corresponding increase in public concern media attention to job stress and its impact upon worker health and well-being. This article provides a summary of the relevant scientific and medical literature on this topic for practitioners and policy-makers. It presents a primer on job stress concepts, an overview of the evidence linking job stress and common mental disorders, a summary of the intervention research on ways to prevent and control job stress, and a discussion of the strengths and weakness of the evidence base. We conclude that there is strong evidence linking job stress and common mental disorders, and that it is a substantial problem on the population level. On a positive note, however, the job stress intervention evidence also shows that the problem is preventable and can be effectively addressed by a combination of work- and worker-directed intervention.

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We present a comparative analysis of patterns of exposure to job stressors and stress-related workers’ compensation (WC) claims to provide an evaluation of the adequacy of claims-driven policy and practice. We assessed job strain prevalence in a 2003 population-based survey of Victorian [Australia] workers and compared these results with stress-related WC statistics for the same year. Job strain prevalence was higher among females than males, and elevated among lower vs. higher occupational skill levels. In comparison, claims were higher among females than males, but primarily among higher skill-level workers. There was some congruence between exposure and WC claims patterns. Highly exposed groups in lower socio-economic positions were underrepresented in claims statistics, suggesting that the WC insurance perspective substantially underestimates the job stress problems for these groups. Thus to provide a sufficient evidence base for equitable policy and practice responses to this growing public health problem, exposure or health outcome data are needed as an essential complement to claims statistics.

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As mobile devices have become the personal information-processing interface of choice, many individuals seem to swiftly follow fashion. Yet, the literature is silent on how early adopters of mobile devices overcome uncertainties related to shifts in technology. Based on purposive sampling, this paper presents detailed insights into why and how five closely related individuals made the decision to adopt the iPhone before it was available through traditional supply chains. Focusing on the role played by social networks, we analyze how adoption threshold, opinion leaders, social contagion, and social learning shaped adoption behaviors and outcomes. The analyses confirm that network structures impact the early decision to accept the iPhone; they show that when facing uncertainty, adoption decisions emerged as a combined result of individual adoption reflections and major influences from the social network as well as behaviors observed within the network, and, they reveal interesting behaviors that differed from expectations. In conclusion, we discuss implications for both theory and practice.

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As mobile devices have become the personal information-processing interface of choice, many individuals seem to swiftly follow fashion. Yet, the literature is silent on how early adopters of mobile devices overcome uncertainties related to shifts in technology. Based on purposive sampling, this paper presents detailed insights into why and how five closely related individuals made the decision to adopt the iPhone before it was available through traditional supply chains. Focusing on the role played by social networks, we analyze how adoption threshold, opinion leaders, social contagion, and social learning shaped adoption behaviors and outcomes. The analyses confirm that network structures impacted the early decision to accept the iPhone; they show that when facing uncertainty, adoption decisions emerged as a combined result of individual adoption reflections and major influences from the social network as well as behaviors observed within the network; and, they reveal interesting behaviors that differed from expectations. In conclusion, we discuss implications for both theory and practice.

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To examine whether positive mental health (PMH)-a positively focused well-being construct-moderates the job stress-distress relationship.

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We examine the relationship between atmospheric and water pollution, traffic congestion, access to parkland and personal well-being using a survey administered across six Chinese cities in 2007. In contrast to existing studies of well-being determinants by economists which typically employ single-item indicators, we use the Personal Well-being Index (PWI). We also employ the Job Satisfaction Survey (JSS) to measure job satisfaction, which is one of the variables for which we control when examining the relationship between environmental surroundings and personal well-being. Previous research by psychologists has shown the PWI and JSS to have good psychometric properties in western and Chinese samples. A robust finding is that in cities with higher levels of atmospheric pollution and traffic congestion, respondents report lower levels of personal well-being ceteris paribus. Specifically, we find that a one standard deviation increase in suspended particles or sulphur dioxide emissions is roughly equivalent to a 12-13% reduction in average monthly income in the six cities. © 2010 Springer Science+Business Media, LLC.

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With the rapid development of smartphones and mobile Internet technology, we witness an overwhelming growth of mobile social networks (MSN), which is a type of social network, forming virtual communities among people with similar interests or commonalities. In MSNs, users play a crucial role for their development, deployment and success. Understanding the MSN user behavior therefore attracts interests of different entities - ISPs, service providers, and researchers. However, it is hard to gather a comprehensive real data set, little is known and even less has been published about MSN user activities. In this paper, we focus on analyzing MSN user behavior from the perspective of ISP network, which is seldom reported in literature. Based on the real data set collected from the mobile network gateway of a major mobile carrier who has more than five million subscribers, we present an in-depth user behavior analysis of four popular social networks. We study the MSN user behavior from six aspects: user requests, active online time, sessions, inter-session, the number of requests in a session, and inter-request. We found that power law and lognormal are two popular features of the studied objects, and exposed some interesting findings as well. We hope our work could be helpful for ISPs, MSN content providers, and researchers. © 2014 IEEE.

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Predicting hydrogen sulphide concentration in sewer network through modelling tools will be beneficial for many stakeholders to design appropriate mitigation strategies. However, the hydrogen sulphide modelling in a sewer network is crucially dependent on the hydraulic modelling of the sewer. The establishment of precise hydrogen sulphide and hydraulic modelling however requires detailed and accurate information about the sewer network structure and the model parameters. This paper outlines a novel approach for the development of hydraulic and hydrogen sulphide modelling to predict the concentration of hydrogen sulphide in sewer network. The approach combines the calculation of wastewater generation and implementation of flow routing on the EPA SWMM 5.0 platform to allow hydrodynamic simulations. Dynamic wave routing is used for hydraulic simulations. It is considered to be the best approach to route existing/old sewer flow. The build-up of hydrogen sulphide model includes the empirical models of hydrogen sulphide generation and emission. Trial of the model was conducted to simulate a sewer network in Seoul, South Korea with some hypothetical data. Further analysis on the use of chemical dosing on the sewer pipe was also performed by the model. Promising results have been obtained through the model, however calibration and validation of the model is required. The presented methodology provides a possibility of the free platform SWMM to be used as a prediction tool of hydrogen sulphide generation. © 2014 © 2014 Balaban Desalination Publications. All rights reserved.