723 resultados para Mining Industry


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Objective: To explore fly-in fly-out (FIFO) mining workers' attitudes towards the leisure time they spend in mining camps, the recreational and social aspects of mining camp culture, the camps' communal and recreational infrastructure and activities, and implications for health. Design: In-depth semistructured interviews. Setting: Individual interviews at locations convenient for each participant. Participants: A total of seven participants, one female and six males. The age group varied within 20–59 years. Marital status varied across participants. Main outcome measures: A qualitative approach was used to interview participants, with responses thematically analysed. Findings highlight how the recreational infrastructure and activities at mining camps impact participants' enjoyment of the camps and their feelings of community and social inclusion. Results: Three main areas of need were identified in the interviews, as follows: (i) on-site facilities and activities; (ii) the role of infrastructure in facilitating a sense of community; and (iii) barriers to social interaction. Conclusion: Recreational infrastructure and activities enhance the experience of FIFO workers at mining camps. The availability of quality recreational facilities helps promote social interaction, provides for greater social inclusion and improves the experience of mining camps for their temporary FIFO residents. The infrastructure also needs to allow for privacy and individual recreational activities, which participants identified as important emotional needs. Developing appropriate recreational infrastructure at mining camps would enhance social interactions among FIFO workers, improve their well-being and foster a sense of community. Introducing infrastructure to promote social and recreational activities could also reduce alcohol-related social exclusion.

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In the Australian fashion industry, few fashion brands have intervened in the design of their products or the systems around their product to tackle environmental pollution and waste. Instead, support of charities (whether social or environmental) has become conflated with sustainability in the eyes of the public. Thus it is difficult to assess with any accuracy fashion brands’ response to sustainability. This article aims to address this through proposing a categorization system to structure the various interventions that a company may make. This system is applied to two case studies, analysing campaigns that respond to environmental sustainability by two established Australian brands, Country Road and Billabong. The case studies demonstrate how the interventions employed by a company, at least in the Australian context, are carefully developed to align with their brand story, revealing the interplay between the intangible aspects of a brand’s positioning and the tangible, measurable impacts of their garments.

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The construction workforce in Hong Kong is experiencing a severe ageing problem and labour shortage. One initiative to enhance the supply of manpower is to assist ethnic minorities joining the industry. It is foreseeable that the percentage of ethnic minorities in the construction workforce will keep increasing. Statistics show that ethnic minorities were nearly 30% more likely to have work-related injuries than local workers in some developed countries. However, official statistics on the safety of ethnic minorities are not available in Hong Kong. A search in newspaper archive revealed that ethnic minorities in the construction industry of Hong Kong are subjected to higher fatality rate than local workers, just as is the case in many developed countries. This reflects that the safety of ethnic minorities has not received the attention it rightly deserves. Safety communication has been one of the key factors leading to accidents. Safety communication barriers of ethnic minorities impede them from receiving safety training and acquiring safety information effectively. Research towards improving the safety communication of ethnic minorities in the construction industry of Hong Kong becomes more urgent. This paper will provides an initial report on a research project which focuses on improving the safety communication of ethnic minorities in the construction industry of Hong Kong. Quantitative and qualitative research methods including Social Network Analysis (SNA) applied in conducting the research are first discussed. Preliminary statistics of construction accidents related to ethnic minorities in Hong Kong are then presented.

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Due to the availability of huge number of web services, finding an appropriate Web service according to the requirements of a service consumer is still a challenge. Moreover, sometimes a single web service is unable to fully satisfy the requirements of the service consumer. In such cases, combinations of multiple inter-related web services can be utilised. This paper proposes a method that first utilises a semantic kernel model to find related services and then models these related Web services as nodes of a graph. An all-pair shortest-path algorithm is applied to find the best compositions of Web services that are semantically related to the service consumer requirement. The recommendation of individual and composite Web services composition for a service request is finally made. Empirical evaluation confirms that the proposed method significantly improves the accuracy of service discovery in comparison to traditional keyword-based discovery methods.

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It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of large scale terms and data patterns. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, there has been often held the hypothesis that pattern-based methods should perform better than term-based ones in describing user preferences; yet, how to effectively use large scale patterns remains a hard problem in text mining. To make a breakthrough in this challenging issue, this paper presents an innovative model for relevance feature discovery. It discovers both positive and negative patterns in text documents as higher level features and deploys them over low-level features (terms). It also classifies terms into categories and updates term weights based on their specificity and their distributions in patterns. Substantial experiments using this model on RCV1, TREC topics and Reuters-21578 show that the proposed model significantly outperforms both the state-of-the-art term-based methods and the pattern based methods.

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The Hong Kong construction industry is currently facing ageing problem and labour shortage. There are opportunities for employing ethnic minority construction workers to join this hazardous industry. These ethnic minority workers are prone to accidents due to communication barriers. Safety communication is playing an important role for avoiding the accidents on construction sites. However, the ethnic minority workers are not very fluent in the local language and facing safety communication problems while working with local workers. Social network analysis (SNA), being an effective tool to identify the safety communication flow on the construction site, is used to attain the measures of safety communication like centrality, density and betweenness within the ethnic minorities and local workers, and to generate sociograms that visually represent communication pattern within the effective and ineffective safety networks. The aim of this paper is to present the application of SNA for improving the safety communication of ethnic minorities in the construction industry of Hong Kong. The paper provides the theoretical background of SNA approaches for the data collection and analysis using the software UCINET and NetDraw, to determine the predominant safety communication network structure and pattern of ethnic minorities on site.

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The importance of firms’ adaptation processes is prominent in today’s business environment which is characterised by ever changing customers, technologies, and competition. Ever since Schumpeter’s (1942) classic work strategic renewal has been found crucial for firms’ adaptation to environmental change. The role of strategic renewal in firms’ adaptation processes includes development of capabilities for the purpose of sustainability of competitive advantage against environmental changes.

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Aligned with the decline of Marshalian view of industry as constituting homogeneous set of firms, the new perspective is emerging by concentrating more on dynamics of sectors as the building block of industrial changes. Based on new assumptions, much of the action in terms of strategy, technology, and knowledge development does not happen either among firms within a stable industry, or through the growth or decline of certain sectors compared to others. Instead, the action happens in terms of the definition, redefinition, drawing, and redrawing of the very nature of these sectors. Technology does not progress and develop within a sector; rather it shapes (and is shaped by) the encompassing architecture of multiple sectors.

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Protein adsorption at solid-liquid interfaces is critical to many applications, including biomaterials, protein microarrays and lab-on-a-chip devices. Despite this general interest, and a large amount of research in the last half a century, protein adsorption cannot be predicted with an engineering level, design-orientated accuracy. Here we describe a Biomolecular Adsorption Database (BAD), freely available online, which archives the published protein adsorption data. Piecewise linear regression with breakpoint applied to the data in the BAD suggests that the input variables to protein adsorption, i.e., protein concentration in solution; protein descriptors derived from primary structure (number of residues, global protein hydrophobicity and range of amino acid hydrophobicity, isoelectric point); surface descriptors (contact angle); and fluid environment descriptors (pH, ionic strength), correlate well with the output variable-the protein concentration on the surface. Furthermore, neural network analysis revealed that the size of the BAD makes it sufficiently representative, with a neural network-based predictive error of 5% or less. Interestingly, a consistently better fit is obtained if the BAD is divided in two separate sub-sets representing protein adsorption on hydrophilic and hydrophobic surfaces, respectively. Based on these findings, selected entries from the BAD have been used to construct neural network-based estimation routines, which predict the amount of adsorbed protein, the thickness of the adsorbed layer and the surface tension of the protein-covered surface. While the BAD is of general interest, the prediction of the thickness and the surface tension of the protein-covered layers are of particular relevance to the design of microfluidics devices.