Profiling HRM in innovative firms

Effective people management is essential to successful innovation, however no single human resource function or practice can facilitate the development of innovation capacity in an organization. Several studies have argued that different bundles or configurations of human resource practices can improve innovation performance, but there is little empirically based research that provides details of the practices utilized by different types of innovative firms. In this exploratory, qualitative study of innovative Danish firms we examine the profiles of human resource practices evident in a sample of firms recognized for their innovative performance. In examining these profiles, we analyze how characteristics of the organizations, namely their size and the nature of industry specific core capabilities, influence the human resource practices used to support innovation. Our initial findings indicate that in this sample of firms size is not a factor but knowledge-intensive firms have notably different profiles of human resource practices to technology-based firms.

Interaction, privacy and profiling considerations in local mobile social software : a prototype agile ride share system

Agile ridesharing aims to utilise the capability of social networks and mobile phones to facilitate people to share vehicles and travel in real time. However the application of social networking technologies in local communities to address issues of personal transport faces significant design challenges. In this paper we describe an iterative design-based approach to exploring this problem and discuss findings from the use of an early prototype. The findings focus upon interaction, privacy and profiling. Our early results suggest that explicitly entering information such as ride data and personal profile data into formal fields for explicit computation of matches, as is done in many systems, may not be the best strategy. It might be preferable to support informal communication and negotiation with text search techniques.

Profiling the offence patterns of disqualified drink drivers in Queensland, Australia.

While unlicensed driving does not play a direct causative role in road crashes, it represents a major problem for road safety. A particular subgroup of concern is those offenders who continue to drive after having their licence disqualified for drink driving. Surveys of disqualified drivers suggest that driving among this group is relatively common. Method This paper reports findings from an analysis of the driving records of over 545,000 Queensland drivers who experienced a licence sanction between January 2003 and December 2008. The sample included drivers who were disqualified by a court (e.g., for drink driving); those who licence had been suspended administratively (e.g., for accumulation of demerit points); and those who were placed on a restricted licence. Results Overall, 95,461 of the drivers in the sample were disqualified from driving for a drink driving offence. During the period, these drivers were issued with a total of 2,644,619 traffic infringements with approximately 12% (n = 8, 095) convicted of a further drink driving offence while disqualified. Other traffic offences detected during this period including unlicensed driving (18%), driving an unregistered vehicle (27%), speeding (21%), dangerous driving (36%), mobile phone use (35%), non-restraint use (32%), and other moving violation (23%). Offending behaviour was more common among men than women. Conclusions While licence disqualification has previously been shown to be a relatively effective sanction for managing the behaviour of drink driving offenders, the results of the current study highlight that it is a far from perfect tool since many offenders continue to commit both drink driving and other traffic offences while disqualified. As such, this study highlights the ongoing need to enhance the detection of disqualified and unlicensed driving in order to deter this behaviour.

Chlamydia muridarum major outer membrane protein-specific antibodies inhibit in vitro infection but enhance pathology in vivo

PROBLEM Chlamydia trachomatis is a significant worldwide health problem, and the often-asymptomatic disease can result in infertility. To develop a successful vaccine, a complete understanding of the immune response to chlamydial infection and development of genital tract pathology is required. METHOD OF STUDY We utilized the murine genital model of chlamydial infection. Mice were immunized with chlamydial major outer membrane protein, and vaginal lavage was assessed for the presence of neutralizing antibodies. These samples were then pre-incubated with Chlamydia muridarum and administered to the vaginal vaults of immune-competent female BALB/c mice to determine the effect on infection. RESULTS The administration of C. muridarum in conjunction with neutralizing antibodies reduced the numbers of mice infected, but a surprising finding was that this accelerated the development of severe oviduct pathology. CONCLUSION Antibodies play an under-recognized role in chlamydial infection and pathology development, which possibly involves interaction with Th1 immunity.

Profiling outdoor leadership

This paper examines the role outdoor recreation and education plays in the development of generic leaders who have a positive relationship to the natural world. Three questionnaires (Multifactor Leadership Questionnaire - MLQ; the New Ecological Paradigm Scale - NEP; and the Connectedness to Nature Scale - CNS) were administered online to 104 international outdoor leaders through five online networks. The three instruments assessed the nexus of transformational leadership theory and outdoor leadership. A descriptive analysis of early findings from the project are outlined in this paper. The results can be viewed as an appropriate platform for understanding outdoor recreation and education leaders’ ecological perspectives and the generic, transformational leadership skills.

Modulation of depth-dependent properties in tissue-engineered cartilage with a semi-permeable membrane and perfusion : a continuum model of matrix metabolism and transport

The functional properties of cartilaginous tissues are determined predominantly by the content, distribution, and organization of proteoglycan and collagen in the extracellular matrix. Extracellular matrix accumulates in tissue-engineered cartilage constructs by metabolism and transport of matrix molecules, processes that are modulated by physical and chemical factors. Constructs incubated under free-swelling conditions with freely permeable or highly permeable membranes exhibit symmetric surface regions of soft tissue. The variation in tissue properties with depth from the surfaces suggests the hypothesis that the transport processes mediated by the boundary conditions govern the distribution of proteoglycan in such constructs. A continuum model (DiMicco and Sah in Transport Porus Med 50:57-73, 2003) was extended to test the effects of membrane permeability and perfusion on proteoglycan accumulation in tissue-engineered cartilage. The concentrations of soluble, bound, and degraded proteoglycan were analyzed as functions of time, space, and non-dimensional parameters for several experimental configurations. The results of the model suggest that the boundary condition at the membrane surface and the rate of perfusion, described by non-dimensional parameters, are important determinants of the pattern of proteoglycan accumulation. With perfusion, the proteoglycan profile is skewed, and decreases or increases in magnitude depending on the level of flow-based stimulation. Utilization of a semi-permeable membrane with or without unidirectional flow may lead to tissues with depth-increasing proteoglycan content, resembling native articular cartilage.

User profiling based on folksonomy information in Web 2.0 for personalized recommender systems

Information overload has become a serious issue for web users. Personalisation can provide effective solutions to overcome this problem. Recommender systems are one popular personalisation tool to help users deal with this issue. As the base of personalisation, the accuracy and efficiency of web user profiling affects the performances of recommender systems and other personalisation systems greatly. In Web 2.0, the emerging user information provides new possible solutions to profile users. Folksonomy or tag information is a kind of typical Web 2.0 information. Folksonomy implies the users‘ topic interests and opinion information. It becomes another source of important user information to profile users and to make recommendations. However, since tags are arbitrary words given by users, folksonomy contains a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise makes it difficult to profile users accurately or to make quality recommendations. This thesis investigates the distinctive features and multiple relationships of folksonomy and explores novel approaches to solve the tag quality problem and profile users accurately. Harvesting the wisdom of crowds and experts, three new user profiling approaches are proposed: folksonomy based user profiling approach, taxonomy based user profiling approach, hybrid user profiling approach based on folksonomy and taxonomy. The proposed user profiling approaches are applied to recommender systems to improve their performances. Based on the generated user profiles, the user and item based collaborative filtering approaches, combined with the content filtering methods, are proposed to make recommendations. The proposed new user profiling and recommendation approaches have been evaluated through extensive experiments. The effectiveness evaluation experiments were conducted on two real world datasets collected from Amazon.com and CiteULike websites. The experimental results demonstrate that the proposed user profiling and recommendation approaches outperform those related state-of-the-art approaches. In addition, this thesis proposes a parallel, scalable user profiling implementation approach based on advanced cloud computing techniques such as Hadoop, MapReduce and Cascading. The scalability evaluation experiments were conducted on a large scaled dataset collected from Del.icio.us website. This thesis contributes to effectively use the wisdom of crowds and expert to help users solve information overload issues through providing more accurate, effective and efficient user profiling and recommendation approaches. It also contributes to better usages of taxonomy information given by experts and folksonomy information contributed by users in Web 2.0.

Parallel user profiling based on folksonomy for Large Scaled Recommender Systems : an implimentation of Cascading MapReduce

The Large scaled emerging user created information in web 2.0 such as tags, reviews, comments and blogs can be used to profile users’ interests and preferences to make personalized recommendations. To solve the scalability problem of the current user profiling and recommender systems, this paper proposes a parallel user profiling approach and a scalable recommender system. The current advanced cloud computing techniques including Hadoop, MapReduce and Cascading are employed to implement the proposed approaches. The experiments were conducted on Amazon EC2 Elastic MapReduce and S3 with a real world large scaled dataset from Del.icio.us website.

Profiling off-road motorcycle and all-terrain riders in Queensland, Australia

The current research aimed to profile off-road riders to identify specific sub-groups in relation to their risk-related behaviours and perceptions. A total of 235 adults from the Australian state of Queensland who had ridden a motorcycle or ATV off-road in the last 12 months were recruited. A cluster analysis was applied to the survey data. Two distinct clusters of riders were identified, which corresponded with the self-report of injury from an off-road riding crash in the prior 12 months. The injured cluster had a significantly higher mean risk propensity and use of safety equipment, though did not differ on self-reported risk taking. The injured cluster as a whole included a higher percentage of males, was younger, and rode more often for recreational or competitive purposes than the non-crash involved cluster. The results indicate that the crash cluster may be both more aware of the potential risks of riding and more willing to ride in a riskier manner.

Whole-proteome phylogeny of large dsDNA viruses and parvoviruses through a composition vector method related to dynamical language model

Background The vast sequence divergence among different virus groups has presented a great challenge to alignment-based analysis of virus phylogeny. Due to the problems caused by the uncertainty in alignment, existing tools for phylogenetic analysis based on multiple alignment could not be directly applied to the whole-genome comparison and phylogenomic studies of viruses. There has been a growing interest in alignment-free methods for phylogenetic analysis using complete genome data. Among the alignment-free methods, a dynamical language (DL) method proposed by our group has successfully been applied to the phylogenetic analysis of bacteria and chloroplast genomes. Results In this paper, the DL method is used to analyze the whole-proteome phylogeny of 124 large dsDNA viruses and 30 parvoviruses, two data sets with large difference in genome size. The trees from our analyses are in good agreement to the latest classification of large dsDNA viruses and parvoviruses by the International Committee on Taxonomy of Viruses (ICTV). Conclusions The present method provides a new way for recovering the phylogeny of large dsDNA viruses and parvoviruses, and also some insights on the affiliation of a number of unclassified viruses. In comparison, some alignment-free methods such as the CV Tree method can be used for recovering the phylogeny of large dsDNA viruses, but they are not suitable for resolving the phylogeny of parvoviruses with a much smaller genome size.