Project management deployment : the role of cultural factors

This paper investigates the role of cultural factors as possible partial explanation of the disparity in terms of Project Management Deployment observed between various studied countries. The topic of culture has received increasing attention in the management literature in general during the last decades and in the Project Management literature in particular during the last few years. The globalization of businesses and worldwide Governmental / International organizations collaborations drives this interest in the national culture to increase more and more. Based on Hofstede national culture framework, the study hypothesizes and tests the impact of the culture and development of the country on the PM Deployment. 74 countries are selected to conduct a correlation and regression analysis between Hofstede’s national culture dimensions and the used PM Deployment indicator. The results show the relations between various national culture dimensions and development indicator (GDP/Capita) on the Project Management Deployment levels of the considered countries.

A Bias in ML Estimates of Branch Lengths in the Presence of Multiple Signals

Sequence data often have competing signals that are detected by network programs or Lento plots. Such data can be formed by generating sequences on more than one tree, and combining the results, a mixture model. We report that with such mixture models, the estimates of edge (branch) lengths from maximum likelihood (ML) methods that assume a single tree are biased. Based on the observed number of competing signals in real data, such a bias of ML is expected to occur frequently. Because network methods can recover competing signals more accurately, there is a need for ML methods allowing a network. A fundamental problem is that mixture models can have more parameters than can be recovered from the data, so that some mixtures are not, in principle, identifiable. We recommend that network programs be incorporated into best practice analysis, along with ML and Bayesian trees.

The role of insulin in advanced prostate cancer

Advanced prostate cancer is a common and generally incurable disease. Androgen deprivation therapy is used to treat advanced prostate cancer with good benefits to quality of life and regression of disease. Prostate cancer invariably progresses however despite ongoing treatment, to a castrate resistant state. Androgen deprivation is associated with a form of metabolic syndrome, which includes insulin resistance and hyperinsulinaemia. The mitogenic and anti-apoptotic properties of insulin acting through the insulin and hybrid insulin/IGF-1 receptors seem to have positive effects on prostate tumour growth. This pilot study was designed to assess any correlation between elevated insulin levels and progression to castrate resistant prostate cancer. Methods: 36 men receiving ADT for advanced prostate cancer were recruited, at various stages of their treatment, along with 47 controls, men with localised prostate cancer pre-treatment. Serum measurements of C-peptide (used as a surrogate marker for insulin production) were performed and compared between groups. Correlation between serum C-peptide level and time to progression to castrate resistant disease was assessed. Results: There was a significant elevation of C-peptide levels in the ADT group (mean = 1639pmol/L)) compared to the control group (mean = 1169pmol/L), with a p-value of 0.025. In 17 men with good initial response to androgen deprivation, a small negative trend towards earlier progression to castrate resistance with increasing C-peptide level was seen in the ADT group (r = -0.050), however this did not reach statistical significance (p>0.1). Conclusions: This pilot study confirms an increase in serum C-peptide levels in men receiving ADT for advance prostate cancer. A non-significant, but negative trend towards earlier progression to castrate resistance with increasing C-peptide suggests the need for a formal prospective study assessing this hypothesis.

The 'obesity paradox' in chronic obstructive pulmonary disease

Poor nutritional status in chronic obstructive pulmonary disease (COPD) is associated with increased mortality independently of disease-severity (Collins et al).1 Epidemiological studies have suggested a protective role of obesity against mortality in COPD (Vestbo et al)2 which is contrary to data from the general population where obesity is associated with decreased life expectancy. This relationship has been referred to as the ‘obesity paradox’ and has been demonstrated in a number of chronic wasting conditions (Kalantar-Zadeh et al).3 This study investigated the existence of the obesity paradox in outpatients with COPD by examining the effect of body mass index (BMI) on 1-year healthcare use and clinical outcome in terms of hospital admission rates, length of hospital stay, outpatient appointments and mortality. BMI was assessed in 424 outpatients with COPD, with measurements performed by specialist respiratory nurses during outpatient clinics. 1-year healthcare use was retrospectively collected from the date of BMI measurement. Abstract S163 Table 1 Patients classified as overweight (25.0–29.9 kg/m2) or obese (>30 kg/m2) experienced significantly fewer emergency hospital admissions, as well as a reduced length of hospital stay, in comparison to normal weight (20.0–24.9 kg/m2) or underweight (<20 kg/m2) outpatients. There was a significant negative trend between BMI classification and mortality. This study supports the existence of the ‘obesity paradox’ in COPD, not only in relation to reduced 1 year mortality rates but also in terms of reduced emergency hospital admissions and reduced length of hospital stay.

Early-mid Cretaceous tectonic evolution of eastern Gondwana : from silicic LIP magmatism to continental rupture

The Early–mid Cretaceous marks the confluence of three major continental-scale events in eastern Gondwana: (1) the emplacement of a Silicic Large Igneous Province (LIP) near the continental margin; (2) the volcaniclastic fill, transgression and regression of a major epicontinental seaway developed over at least a quarter of the Australian continent; and (3) epeirogenic uplift, exhumation and continental rupturing culminating in the opening of the Tasman Basin c. 84 Ma. The Whitsunday Silicic LIP event had widespread impact, producing both substantial extrusive volumes of dominantly silicic pyroclastic material and coeval first-cycle volcanogenic sediment that accumulated within many eastern Australian sedimentary basins, and principally in the Great Australian Basin system (>2 Mkm3 combined volume). The final pulse of volcanism and volcanogenic sedimentation at c. 105–95 Ma coincided with epicontinental seaway regression, which shows a lack of correspondence with the global sea-level curve, and alternatively records a wider, continental-scale effect of volcanism and rift tectonism. Widespread igneous underplating related to this LIP event is evident from high paleogeothermal gradients and regional hydrothermal fluid flow detectable in the shallow crust and over a broad region. Enhanced CO2 fluxing through sedimentary basins also records indirectly, large-scale, LIP-related mafic underplating. A discrete episode of rapid crustal cooling and exhumation began c. 100–90 Ma along the length of the eastern Australian margin, related to an enhanced phase of continental rifting that was largely amagmatic, and probably a switch from wide–more narrow rift modes. Along-margin variations in detachment fault architecture produced narrow (SE Australia) and wide continental margins with marginal, submerged continental plateaux (NE Australia). Long-lived NE-trending cross-orogen lineaments controlled the switch from narrow to wide continental margin geometries.

A picture is worth a thousand words : a cross-curricular approach to learning about visuals in STEM

Visuals are a central feature of STEM in all levels of education and many areas of employment. The wide variety of visuals that students are expected to master in STEM prevents an approach that aims to teach students about every type of visual that they may encounter. This paper proposes a pedagogy that can be applied across year levels and learning areas, allowing a school-wide, cross-curricular, approach to teaching about visual, that enhances learning in STEM and all other learning areas. Visuals are classified into six categories based on their properties, unlike traditional methods that classify visuals according to purpose. As visuals in the same category share common properties, students are able to transfer their knowledge from the familiar to unfamiliar in each category. The paper details the classification and proposes some strategies that can be can be incorporated into existing methods of teaching students about visuals in all learning areas. The approach may also assist students to see the connections between the different learning areas within and outside STEM.

Scene invariant multi camera crowd counting

Automated crowd counting has become an active field of computer vision research in recent years. Existing approaches are scene-specific, as they are designed to operate in the single camera viewpoint that was used to train the system. Real world camera networks often span multiple viewpoints within a facility, including many regions of overlap. This paper proposes a novel scene invariant crowd counting algorithm that is designed to operate across multiple cameras. The approach uses camera calibration to normalise features between viewpoints and to compensate for regions of overlap. This compensation is performed by constructing an 'overlap map' which provides a measure of how much an object at one location is visible within other viewpoints. An investigation into the suitability of various feature types and regression models for scene invariant crowd counting is also conducted. The features investigated include object size, shape, edges and keypoints. The regression models evaluated include neural networks, K-nearest neighbours, linear and Gaussian process regresion. Our experiments demonstrate that accurate crowd counting was achieved across seven benchmark datasets, with optimal performance observed when all features were used and when Gaussian process regression was used. The combination of scene invariance and multi camera crowd counting is evaluated by training the system on footage obtained from the QUT camera network and testing it on three cameras from the PETS 2009 database. Highly accurate crowd counting was observed with a mean relative error of less than 10%. Our approach enables a pre-trained system to be deployed on a new environment without any additional training, bringing the field one step closer toward a 'plug and play' system.

Real - time prediction of rainfall induced instability of residual soil slopes associated with deep cracks

The early warning based on real-time prediction of rain-induced instability of natural residual slopes helps to minimise human casualties due to such slope failures. Slope instability prediction is complicated, as it is influenced by many factors, including soil properties, soil behaviour, slope geometry, and the location and size of deep cracks in the slope. These deep cracks can facilitate rainwater infiltration into the deep soil layers and reduce the unsaturated shear strength of residual soil. Subsequently, it can form a slip surface, triggering a landslide even in partially saturated soil slopes. Although past research has shown the effects of surface-cracks on soil stability, research examining the influence of deep-cracks on soil stability is very limited. This study aimed to develop methodologies for predicting the real-time rain-induced instability of natural residual soil slopes with deep cracks. The results can be used to warn against potential rain-induced slope failures. The literature review conducted on rain induced slope instability of unsaturated residual soil associated with soil crack, reveals that only limited studies have been done in the following areas related to this topic: - Methods for detecting deep cracks in residual soil slopes. - Practical application of unsaturated soil theory in slope stability analysis. - Mechanistic methods for real-time prediction of rain induced residual soil slope instability in critical slopes with deep cracks. Two natural residual soil slopes at Jombok Village, Ngantang City, Indonesia, which are located near a residential area, were investigated to obtain the parameters required for the stability analysis of the slope. A survey first identified all related field geometrical information including slope, roads, rivers, buildings, and boundaries of the slope. Second, the electrical resistivity tomography (ERT) method was used on the slope to identify the location and geometrical characteristics of deep cracks. The two ERT array models employed in this research are: Dipole-dipole and Azimuthal. Next, bore-hole tests were conducted at different locations in the slope to identify soil layers and to collect undisturbed soil samples for laboratory measurement of the soil parameters required for the stability analysis. At the same bore hole locations, Standard Penetration Test (SPT) was undertaken. Undisturbed soil samples taken from the bore-holes were tested in a laboratory to determine the variation of the following soil properties with the depth: - Classification and physical properties such as grain size distribution, atterberg limits, water content, dry density and specific gravity. - Saturated and unsaturated shear strength properties using direct shear apparatus. - Soil water characteristic curves (SWCC) using filter paper method. - Saturated hydraulic conductivity. The following three methods were used to detect and simulate the location and orientation of cracks in the investigated slope: (1) The electrical resistivity distribution of sub-soil obtained from ERT. (2) The profile of classification and physical properties of the soil, based on laboratory testing of soil samples collected from bore-holes and visual observations of the cracks on the slope surface. (3) The results of stress distribution obtained from 2D dynamic analysis of the slope using QUAKE/W software, together with the laboratory measured soil parameters and earthquake records of the area. It was assumed that the deep crack in the slope under investigation was generated by earthquakes. A good agreement was obtained when comparing the location and the orientation of the cracks detected by Method-1 and Method-2. However, the simulated cracks in Method-3 were not in good agreement with the output of Method-1 and Method-2. This may have been due to the material properties used and the assumptions made, for the analysis. From Method-1 and Method-2, it can be concluded that the ERT method can be used to detect the location and orientation of a crack in a soil slope, when the ERT is conducted in very dry or very wet soil conditions. In this study, the cracks detected by the ERT were used for stability analysis of the slope. The stability of the slope was determined using the factor of safety (FOS) of a critical slip surface obtained by SLOPE/W using the limit equilibrium method. Pore-water pressure values for the stability analysis were obtained by coupling the transient seepage analysis of the slope using finite element based software, called SEEP/W. A parametric study conducted on the stability of an investigated slope revealed that the existence of deep cracks and their location in the soil slope are critical for its stability. The following two steps are proposed to predict the rain-induced instability of a residual soil slope with cracks. (a) Step-1: The transient stability analysis of the slope is conducted from the date of the investigation (initial conditions are based on the investigation) to the preferred date (current date), using measured rainfall data. Then, the stability analyses are continued for the next 12 months using the predicted annual rainfall that will be based on the previous five years rainfall data for the area. (b) Step-2: The stability of the slope is calculated in real-time using real-time measured rainfall. In this calculation, rainfall is predicted for the next hour or 24 hours and the stability of the slope is calculated one hour or 24 hours in advance using real time rainfall data. If Step-1 analysis shows critical stability for the forthcoming year, it is recommended that Step-2 be used for more accurate warning against the future failure of the slope. In this research, the results of the application of the Step-1 on an investigated slope (Slope-1) showed that its stability was not approaching a critical value for year 2012 (until 31st December 2012) and therefore, the application of Step-2 was not necessary for the year 2012. A case study (Slope-2) was used to verify the applicability of the complete proposed predictive method. A landslide event at Slope-2 occurred on 31st October 2010. The transient seepage and stability analyses of the slope using data obtained from field tests such as Bore-hole, SPT, ERT and Laboratory tests, were conducted on 12th June 2010 following the Step-1 and found that the slope in critical condition on that current date. It was then showing that the application of the Step-2 could have predicted this failure by giving sufficient warning time.

Back to the future

In the field of education, explicit instruction refers to teacher-­‐centred instruction that is focused on clear behavioural and cognitive goals and outcomes. These in turn are made ‘explicit’ or transparent to learners. Sociologist Basil Bernstein defined explicit instruction as featuring “strong classification” and “strong framing”: clearly defined and boundaried knowledge and skills, and teacher-­‐directed interaction. Explicit instruction is affiliated with but not limited to highly structured, instruction in basic skills in early literacy and numeracy education. It is also used in Australian genre-­‐based approaches to writing that stress the value of “explicit” knowledge of grammar and all textual codes. Several major meta-­‐analyses and reviews have identified explicit instruction as a major instructional approach in contemporary schooling...

The influence of extracellular matrix on the generation of vascularized, engineered, transplantable tissue

In a recently described model for tissue engineering, an arteriovenous loop comprising the femoral artery and vein with interposed vein graft is fabricated in the groin of an adult male rat, placed inside a polycarbonate chamber, and incubated subcutaneously. New vascularized granulation tissue will generate on this loop for up to 12 weeks. In the study described in this paper three different extracellular matrices were investigated for their ability to accelerate the amount of tissue generated compared with a no-matrix control. Poly-D,L-lactic-co-glycolic acid (PLGA) produced the maximal weight of new tissue and vascularization and this peaked at two weeks, but regressed by four weeks. Matrigel was next best. It peaked at four weeks but by eight weeks it also had regressed. Fibrin (20 and 80 mg/ml), by contrast, did not integrate with the generating vascularized tissue and produced less weight and volume of tissue than controls without matrix. The limiting factors to growth appear to be the chamber size and the capacity of the neotissue to integrate with the matrix. Once the sides of the chamber are reached or tissue fails to integrate, encapsulation and regression follow. The intrinsic position of the blood supply within the neotissue has many advantages for tissue and organ engineering, such as ability to seed the construct with stem cells and microsurgically transfer new tissue to another site within the individual. In conclusion, this study has found that PLGA and Matrigel are the best matrices for the rapid growth of new vascularized tissue suitable for replantation or transplantation.

A preliminary framework for DNA barcoding, incorporating the multispecies coalescent

The capacity to identify an unknown organism using the DNA sequence from a single gene has many applications. These include the development of biodiversity inventories (Janzen et al. 2005), forensics (Meiklejohn et al. 2011), biosecurity (Armstrong and Ball 2005), and the identification of cryptic species (Smith et al. 2006). The popularity and widespread use (Teletchea 2010) of the DNA barcoding approach (Hebert et al. 2003), despite broad misgivings (e.g., Smith 2005; Will et al. 2005; Rubinoff et al. 2006), attest to this. However, one major shortcoming to the standard barcoding approach is that it assumes that gene trees and species trees are synonymous, an assumption that is known not to hold in many cases (Pamilo and Nei 1988; Funk and Omland 2003). Biological processes that violate this assumption include incomplete lineage sorting and interspecific hybridization (Funk and Omland 2003). Indeed, simulation studies indicate that the concatenation approach (in which these two processes are ignored) can lead to statistically inconsistent estimation of the species tree (Kubatko and Degnan 2007)...

Chaperone-usher fimbriae of Escherichia coli

Chaperone-usher (CU) fimbriae are adhesive surface organelles common to many Gram-negative bacteria. Escherichia coli genomes contain a large variety of characterised and putative CU fimbrial operons, however, the classification and annotation of individual loci remains problematic. Here we describe a classification model based on usher phylogeny and genomic locus position to categorise the CU fimbrial types of E. coli. Using the BLASTp algorithm, an iterative usher protein search was performed to identify CU fimbrial operons from 35 E. coli (and one Escherichia fergusonnii) genomes representing different pathogenic and phylogenic lineages, as well as 132 Escherichia spp. plasmids. A total of 458 CU fimbrial operons were identified, which represent 38 distinct fimbrial types based on genomic locus position and usher phylogeny. The majority of fimbrial operon types occupied a specific locus position on the E. coli chromosome; exceptions were associated with mobile genetic elements. A group of core-associated E. coli CU fimbriae were defined and include the Type 1, Yad, Yeh, Yfc, Mat, F9 and Ybg fimbriae. These genes were present as intact or disrupted operons at the same genetic locus in almost all genomes examined. Evaluation of the distribution and prevalence of CU fimbrial types among different pathogenic and phylogenic groups provides an overview of group specific fimbrial profiles and insight into the ancestry and evolution of CU fimbriae in E. coli.

The influence of fleet safety climate on occupational driver behaviours

The concepts of traffic safety culture and climate hold considerable impact on road safety outcomes. Data sourced from four Australian organisations revealed a five factor structure that was consistent with previous research, which were: management commitment; work demands; relationships; appropriateness of rules; and communication. Correlation and regression analyses were conducted to identify which aspects of fleet safety climate were related to driver behaviours. The findings suggest that organisations may be able to reduce the likelihood of employees engaging in unsafe driving behaviours as a result of fatigue or distractions through increasing aspects of fleet safety climate, including: management commitment; level of trust; safety communication; appropriateness of work demands; and appropriateness of safety policies and procedures. To assist practitioners in enhancing fleet safety climate and managing occupational road risks, recommendations are made based on these findings, such as fostering a supportive environment of mutual responsibility.

The influence of internal communication on employee engagement: A pilot study

Internal communication is an important concept and integral to internal public relations with links to positive organizational and employee outcomes such as employee engagement. While scholars emphasize the importance of the relationship between internal communication and employee engagement, the association has not been empirically tested. Using surveys and regression analysis this study confirms that internal organizational communication and internal supervisor communication support workplace relationships based on meaning and worth, and have a significant part to play in developing and maintaining optimal employee engagement.

Supervised latent dirichlet allocation models for efficient activity representation

Local spatio-temporal features with a Bag-of-visual words model is a popular approach used in human action recognition. Bag-of-features methods suffer from several challenges such as extracting appropriate appearance and motion features from videos, converting extracted features appropriate for classification and designing a suitable classification framework. In this paper we address the problem of efficiently representing the extracted features for classification to improve the overall performance. We introduce two generative supervised topic models, maximum entropy discrimination LDA (MedLDA) and class- specific simplex LDA (css-LDA), to encode the raw features suitable for discriminative SVM based classification. Unsupervised LDA models disconnect topic discovery from the classification task, hence yield poor results compared to the baseline Bag-of-words framework. On the other hand supervised LDA techniques learn the topic structure by considering the class labels and improve the recognition accuracy significantly. MedLDA maximizes likelihood and within class margins using max-margin techniques and yields a sparse highly discriminative topic structure; while in css-LDA separate class specific topics are learned instead of common set of topics across the entire dataset. In our representation first topics are learned and then each video is represented as a topic proportion vector, i.e. it can be comparable to a histogram of topics. Finally SVM classification is done on the learned topic proportion vector. We demonstrate the efficiency of the above two representation techniques through the experiments carried out in two popular datasets. Experimental results demonstrate significantly improved performance compared to the baseline Bag-of-features framework which uses kmeans to construct histogram of words from the feature vectors.