978 resultados para random wave


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Principal Topic: It is well known that most new ventures suffer from a significant lack of resources, which increases the risk of failure (Shepherd, Douglas and Shanley, 2000) and makes it difficult to attract stakeholders and financing for the venture (Bhide & Stevenson, 1999). The Resource-Based View (RBV) (Barney, 1991; Wernerfelt, 1984) is a dominant theoretical base increasingly drawn on within Strategic Management. While theoretical contributions applying RBV in the domain of entrepreneurship can arguably be traced back to Penrose (1959), there has been renewed attention recently (e.g. Alvarez & Busenitz, 2001; Alvarez & Barney, 2004). This said, empirical work is in its infancy. In part, this may be due to a lack of well developed measuring instruments for testing ideas derived from RBV. The purpose of this study is to develop a measurement scales that can serve to assist such empirical investigations. In so doing we will try to overcome three deficiencies in current empirical measures used for the application of RBV to the entrepreneurship arena. First, measures for resource characteristics and configurations associated with typical competitive advantages found in entrepreneurial firms need to be developed. These include such things as alertness and industry knowledge (Kirzner, 1973), flexibility (Ebben & Johnson, 2005), strong networks (Lee et al., 2001) and within knowledge intensive contexts, unique technical expertise (Wiklund and Shepard, 2003). Second, the RBV has the important limitations of being relatively static and modelled on large, established firms. In that context, traditional RBV focuses on competitive advantages. However, newly established firms often face disadvantages, especially those associated with the liabilities of newness (Aldrich & Auster, 1986). It is therefore important in entrepreneurial contexts to expand to an investigation of responses to competitive disadvantage through an RBV lens. Conversely, recent research has suggested that resource constraints actually have a positive effect on firm growth and performance under some circumstances (e.g., George, 2005; Katila & Shane, 2005; Mishina et al., 2004; Mosakowski, 2002; cf. also Baker & Nelson, 2005). Third, current empirical applications of RBV measured levels or amounts of particular resources available to a firm. They infer that these resources deliver firms competitive advantage by establishing a relationship between these resource levels and performance (e.g. via regression on profitability). However, there is the opportunity to directly measure the characteristics of resource configurations that deliver competitive advantage, such as Barney´s well known VRIO (Valuable, Rare, Inimitable and Organized) framework (Barney, 1997). Key Propositions and Methods: The aim of our study is to develop and test scales for measuring resource advantages (and disadvantages) and inimitability for entrepreneurial firms. The study proceeds in three stages. The first stage developed our initial scales based on earlier literature. Where possible, we adapt scales based on previous work. The first block of the scales related to the level of resource advantages and disadvantages. Respondents were asked the degree to which each resource category represented an advantage or disadvantage relative to other businesses in their industry on a 5 point response scale: Major Disadvantage, Slight Disadvantage, No Advantage or Disadvantage, Slight Advantage and Major Advantage. Items were developed as follows. Network capabilities (3 items) were adapted from (Madsen, Alsos, Borch, Ljunggren & Brastad, 2006). Knowledge resources marketing expertise / customer service (3 items) and technical expertise (3 items) were adapted from Wiklund and Shepard (2003). flexibility (2 items), costs (4 items) were adapted from JIBS B97. New scales were developed for industry knowledge / alertness (3 items) and product / service advantages. The second block asked the respondent to nominate the most important resource advantage (and disadvantage) of the firm. For the advantage, they were then asked four questions to determine how easy it would be for other firms to imitate and/or substitute this resource on a 5 point likert scale. For the disadvantage, they were asked corresponding questions related to overcoming this disadvantage. The second stage involved two pre-tests of the instrument to refine the scales. The first was an on-line convenience sample of 38 respondents. The second pre-test was a telephone interview with a random sample of 31 Nascent firms and 47 Young firms (< 3 years in operation) generated using a PSED method of randomly calling households (Gartner et al. 2004). Several items were dropped or reworded based on the pre-tests. The third stage (currently in progress) is part of Wave 1 of CAUSEE (Nascent Firms) and FEDP (Young Firms), a PSED type study being conducted in Australia. The scales will be tested and analysed with a random sample of approximately 700 Nascent and Young firms respectively. In addition, a judgement sample of approximately 100 high potential businesses in each category will be included. Findings and Implications: The paper will report the results of the main study (stage 3 – currently data collection is in progress) will allow comparison of the level of resource advantage / disadvantage across various sub-groups of the population. Of particular interest will be a comparison of the high potential firms with the random sample. Based on the smaller pre-tests (N=38 and N=78) the factor structure of the items confirmed the distinctiveness of the constructs. The reliabilities are within an acceptable range: Cronbach alpha ranged from 0.701 to 0.927. The study will provide an opportunity for researchers to better operationalize RBV theory in studies within the domain of entrepreneurship. This is a fundamental requirement for the ability to test hypotheses derived from RBV in systematic, large scale research studies.

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Principal Topic The Comprehensive Australian Study of Entrepreneurial Emergence (CAUSEE) represents the first Australian study to employ and extend the longitudinal and large scale systematic research developed for the Panel Study of Entrepreneurial Dynamics (PSED) in the US (Gartner, Shaver, Carter and Reynolds, 2004; Reynolds, 2007). This research approach addresses several shortcomings of other data sets including under coverage; selection bias; memory decay and hindsight bias, and lack of time separation between the assessment of causes and their assumed effects (Johnson et al 2006; Davidsson 2006). However, a remaining problem is that any a random sample of start-ups will be dominated by low potential, imitative ventures. In recognition of this issue CAUSEE supplemented PSED-type random samples with theoretically representative samples of the 'high potential' emerging ventures employing a unique methodology using novel multiple screening criteria. We define new ''high-potential'' ventures as new entrepreneurial innovative ventures with high aspirations and potential for growth. This distinguishes them from those ''lifestyle'' imitative businesses that start small and remain intentionally small (Timmons, 1986). CAUSEE is providing the opportunity to explore, for the first time, if process and outcomes of high potentials differ from those of traditional lifestyle firms. This will allows us to compare process and outcome attributes of the random sample with the high potential over sample of new firms and young firms. The attributes in which we will examine potential differences will include source of funding, and internationalisation. This is interesting both in terms of helping to explain why different outcomes occur but also in terms of assistance to future policymaking, given that high growth potential firms are increasingly becoming the focus of government intervention in economic development policies around the world. The first wave of data of a four year longitudinal study has been collected using these samples, allowing us to also provide some initial analysis on which to continue further research. The aim of this paper therefore is to present some selected preliminary results from the first wave of the data collection, with comparisons of high potential with lifestyle firms. We expect to see owing to greater resource requirements and higher risk profiles, more use of venture capital and angel investment, and more internationalisation activity to assist in recouping investment and to overcome Australia's smaller economic markets Methodology/Key Propositions In order to develop the samples of 'high potential' in the NF and YF categories a set of qualification criteria were developed. Specifically, to qualify, firms as nascent or young high potentials, we used multiple, partly compensating screening criteria related to the human capital and aspirations of the founders as well as the novelty of the venture idea, and venture high technology. A variety of techniques were also employed to develop a multi level dataset of sources to develop leads and firm details. A dataset was generated from a variety of websites including major stakeholders including the Federal and State Governments, Australian Chamber of Commerce, University Commercialisation Offices, Patent and Trademark Attorneys, Government Awards and Industry Awards in Entrepreneurship and Innovation, Industry lead associations, Venture Capital Association, Innovation directories including Australian Technology Showcase, Business and Entrepreneurs Magazines including BRW and Anthill. In total, over 480 industry, association, government and award sources were generated in this process. Of these, 74 discrete sources generated high potentials that fufilled the criteria. 1116 firms were contacted as high potential cases. 331 cases agreed to participate in the screener, with 279 firms (134 nascents, and 140 young firms) successfully passing the high potential criteria. 222 Firms (108 Nascents and 113 Young firms) completed the full interview. For the general sample CAUSEE conducts screening phone interviews with a very large number of adult members of households randomly selected through random digit dialing using screening questions which determine whether respondents qualify as 'nascent entrepreneurs'. CAUSEE additionally targets 'young firms' those that commenced trading from 2004 or later. This process yielded 977 Nascent Firms (3.4%) and 1,011 Young Firms (3.6%). These were directed to the full length interview (40-60 minutes) either directly following the screener or later by appointment. The full length interviews were completed by 594 NF and 514 YF cases. These are the cases we will use in the comparative analysis in this report. Results and Implications The results for this paper are based on Wave one of the survey which has been completed and the data obtained. It is expected that the findings will assist in beginning to develop an understanding of high potential nascent and young firms in Australia, how they differ from the larger lifestyle entrepreneur group that makes up the vast majority of the new firms created each year, and the elements that may contribute to turning high potential growth status into high growth realities. The results have implications for Government in the design of better conditions for the creation of new business, firms who assist high potentials in developing better advice programs in line with a better understanding of their needs and requirements, individuals who may be considering becoming entrepreneurs in high potential arenas and existing entrepreneurs make better decisions.

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Channel measurements and simulations have been carried out to observe the effects of pedestrian movement on multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) channel capacity. An in-house built MIMO-OFDM packet transmission demonstrator equipped with four transmitters and four receivers has been utilized to perform channel measurements at 5.2 GHz. Variations in the channel capacity dynamic range have been analysed for 1 to 10 pedestrians and different antenna arrays (2 × 2, 3 × 3 and 4 × 4). Results show a predicted 5.5 bits/s/Hz and a measured 1.5 bits/s/Hz increment in the capacity dynamic range with the number of pedestrian and the number of antennas in the transmitter and receiver array.

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Dr. Young-Ki Paik directs the Yonsei Proteome Research Center in Seoul, Korea and was elected as the President of the Human Proteome Organization (HUPO) in 2009. In the December 2009 issue of the Current Pharmacogenomics and Personalized Medicine (CPPM), Dr. Paik explains the new field of pharmacoproteomics and the approaching wave of “proteomics diagnostics” in relation to personalized medicine, HUPO’s role in advancing proteomics technology applications, the HUPO Proteomics Standards Initiative, and the future impact of proteomics on medicine, science, and society. Additionally, he comments that (1) there is a need for launching a Gene-Centric Human Proteome Project (GCHPP) through which all representative proteins encoded by the genes can be identified and quantified in a specific cell and tissue and, (2) that the innovation frameworks within the diagnostics industry hitherto borrowed from the genetics age may require reevaluation in the case of proteomics, in order to facilitate the uptake of pharmacoproteomics innovations. He stresses the importance of biological/clinical plausibility driving the evolution of biotechnologies such as proteomics,instead of an isolated singular focus on the technology per se. Dr. Paik earned his Ph.D. in biochemistry from the University of Missouri-Columbia and carried out postdoctoral work at the Gladstone Foundation Laboratories of Cardiovascular Disease, University of California at San Francisco. In 2005, his research team at Yonsei University first identified and characterized the chemical structure of C. elegans dauer pheromone (daumone) which controls the aging process of this nematode. He is interviewed by a multidisciplinary team specializing in knowledge translation, technology regulation, health systems governance, and innovation analysis.

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A method for determination of lactose in food samples by Osteryoung square wave voltammetry (OSWV) was developed. It was based on the nucleophilic addition reaction between lactose and aqua ammonia. The carbonyl group of lactose can be changed into imido group, and this increases the electrochemical activity in reduction and the sensitivity. The optimal condition for the nucleophilic addition reaction was investigated and it was found that in NH4Cl–NH3 buffer of pH 10.1, the linear range between the peak current and the concentration of lactose was 0.6–8.4 mg L−1, and the detection limits was 0.44 mg L−1. The proposed method was applied to the determination of lactose in food samples and satisfactory results were obtained.

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Random Indexing K-tree is the combination of two algorithms suited for large scale document clustering.

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This report presents an analysis of the data from the first wave of the Longitudinal Study of Australian Children (LSAC) to explore the wellbeing of 5,107 children in the infant cohort of the study and the 4,983 children, aged 4 to 5 years, in the child cohort. Wave 1 of LSAC includes measures of multiple aspects of children’s early development. These developmental measures are summarised in the LSAC Outcome Index, a composite measure which includes an overall index as well as three separate domain scores, tapping physical development, social and emotional functioning, and learning and cognitive development.

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In this paper, we consider a time-space fractional diffusion equation of distributed order (TSFDEDO). The TSFDEDO is obtained from the standard advection-dispersion equation by replacing the first-order time derivative by the Caputo fractional derivative of order α∈(0,1], the first-order and second-order space derivatives by the Riesz fractional derivatives of orders β 1∈(0,1) and β 2∈(1,2], respectively. We derive the fundamental solution for the TSFDEDO with an initial condition (TSFDEDO-IC). The fundamental solution can be interpreted as a spatial probability density function evolving in time. We also investigate a discrete random walk model based on an explicit finite difference approximation for the TSFDEDO-IC.

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In 1967 Brisbane Repertory Theatre made a decision that was to change the city's cultural landscape in a significant and lasting way. Faced with crippling theatre rental costs, Brisbane Rep. found a realistic solution by converting one of its properties - an old Queenslander - into a unique theatre space. The theatre-in-the box that emerged, aptly called La Boite, opened on 23 June 1967 with a production of John Osborne's Look Back in Anger. This experimental space excited the imagination of a new, younger audience not previously interested in Brisbane Rep's essentially conservative fare. It attracted a new group of directors and actors keen to be part of a changing repertoire that embraced more radical, non-mainstream productions, some of which were of Australian plays. The decade after 1967 was a period of change and development unprecedented in La Boite's history. Since then the company has sustained and grown its commitment to Australian plays and the commissioning of new works. To what extent was this most significance moment in La Boite's transformational journey influenced by southern 'new waves' of change? With the benefit of hindsight, it is now time for a re-consideration of Brisbane's distinctive contribution to the New Wave.

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Adiabatic compression testing of components in gaseous oxygen is a test method that is utilized worldwide and is commonly required to qualify a component for ignition tolerance under its intended service. This testing is required by many industry standards organizations and government agencies. This paper traces the background of adiabatic compression testing in the oxygen community and discusses the thermodynamic and fluid dynamic processes that occur during rapid pressure surges. This paper is the first of several papers by the authors on the subject of adiabatic compression testing and is presented as a non-comprehensive background and introduction.

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This paper describes the approach taken to the clustering task at INEX 2009 by a group at the Queensland University of Technology. The Random Indexing (RI) K-tree has been used with a representation that is based on the semantic markup available in the INEX 2009 Wikipedia collection. The RI K-tree is a scalable approach to clustering large document collections. This approach has produced quality clustering when evaluated using two different methodologies.

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In cloud computing resource allocation and scheduling of multiple composite web services is an important challenge. This is especially so in a hybrid cloud where there may be some free resources available from private clouds but some fee-paying resources from public clouds. Meeting this challenge involves two classical computational problems. One is assigning resources to each of the tasks in the composite web service. The other is scheduling the allocated resources when each resource may be used by more than one task and may be needed at different points of time. In addition, we must consider Quality-of-Service issues, such as execution time and running costs. Existing approaches to resource allocation and scheduling in public clouds and grid computing are not applicable to this new problem. This paper presents a random-key genetic algorithm that solves new resource allocation and scheduling problem. Experimental results demonstrate the effectiveness and scalability of the algorithm.