Is the planet full? by Ian Goldin (Oxford University Press, Oxford, 2014), pp. 272.

The question ‘is the planet full?’ is not new but it needs a critical assessment to provide a good answer. Obviously, the capacity of the planet should be evaluated in relation to the size and distribution of its human population and how relevant resources of the planet are used and managed. When we are discussing human population and resource management, multidimensional issues such as welfare, technologies and social changes are essential...

Investigating the effectiveness of an ecological approach to learning design in a first year mathematics for engineering unit

This paper reports on the results of a project aimed at creating a research-informed, pedagogically reliable, technology-enhanced learning and teaching environment that would foster engagement with learning. A first-year mathematics for engineering unit offered at a large, metropolitan Australian university provides the context for this research. As part of the project, the unit was redesigned using a framework that employed flexible, modular, connected e-learning and teaching experiences. The researchers, interested in an ecological perspective on educational processes, grounded the redesign principles in probabilistic learning design (Kirschner et al., 2004). The effectiveness of the redesigned environment was assessed through the lens of the notion of affordance (Gibson, 1977,1979, Greeno, 1994, Good, 2007). A qualitative analysis of the questionnaire distributed to students at the end of the teaching period provided insight into factors impacting on the successful creation of an environment that encourages complex, multidimensional and multilayered interactions conducive to learning.

Does size matter? Knowledge-based development of second-order city-regions in Finland

Achieving knowledge-based urban development (KBUD) profoundly depends on not only encouraging the development of economic activities, but also strengthening the societal, environmental and governance bases of city-regions. In recent years, a number of global city-regions have been investigated from the angle of this multidimensional perspective, which has provided a new comprehension in the development processes of primate city-regions. However, there is a knowledge gap in understanding how KBUD works in the second-order city-region (SOCR) context. This warrants more attention as SOCRs potentially help secure balanced development and territorial cohesion. This paper aims to empirically investigate KBUD performances of SOCRs in order to generate new insights. An assessment framework is utilised in the Finnish context, where the findings provide a nationally benchmarked snapshot of the degree of achievements of SOCRs based on numerous KBUD performance areas. The results shed light on the unique Finnish urban and regional development process, and provide lessons for other SOCRs.

Separable spatiotemporal priors for convex reconstruction of time-varying 3D point clouds

Reconstructing 3D motion data is highly under-constrained due to several common sources of data loss during measurement, such as projection, occlusion, or miscorrespondence. We present a statistical model of 3D motion data, based on the Kronecker structure of the spatiotemporal covariance of natural motion, as a prior on 3D motion. This prior is expressed as a matrix normal distribution, composed of separable and compact row and column covariances. We relate the marginals of the distribution to the shape, trajectory, and shape-trajectory models of prior art. When the marginal shape distribution is not available from training data, we show how placing a hierarchical prior over shapes results in a convex MAP solution in terms of the trace-norm. The matrix normal distribution, fit to a single sequence, outperforms state-of-the-art methods at reconstructing 3D motion data in the presence of significant data loss, while providing covariance estimates of the imputed points.

Underlying knowledge of construction management consultants in China

To meet clients/owners’ multidimensional and changing requirements, construction management consultants (CMCs) ought to possess a diverse and dynamic knowledge structure. In China, although the population of CMCs has grown to the point of their being indispensable in the industry, their knowledge structure has not been explored explicitly. The study presented in this paper investigated this by first conducting a comprehensive content analysis of the curricula of the highest ranked construction management university courses in China. This was followed by in-depth interviews with experts, resulting in the identification of 22 main knowledge areas that can be grouped into technology, economy, management and law. A questionnaire survey was then conducted among 115 experienced CMCs to evaluate the current level of knowledge in these areas together with their importance and need-for-improvement. The main findings demonstrate the significance of the identified 22 knowledge areas, and they also need substantial improvement in practice. The research has practical implications for China's CMCs to develop necessary knowledge and the extent to which they need to be improved to provide a better quality of services in future.

Stakeholder analysis of a retirement village development in Australia : Insights from an interdisciplinary workshop

In Australia, the retirement village is regarded as a viable accommodation option for the fast-growing population of older people. Given that diverse stakeholders are involved in the village development process, a comprehensive stakeholder analysis, especially understanding stakeholders' concerns and expectations, is of critical importance to project success. This paper describes a stakeholder analysis based on a typical retirement village. The main methods utilized in this research were case study and an interdisciplinary workshop comprising intensive work sessions and cross-functional review. The participants of this 2014 Brisbane workshop included four village managers from an Australian retirement village developer and eight academics from diverse disciplines from a large university. Through this workshop, 24 village stakeholders and their specific concerns and expectations were identified. Results suggest that both concerns and expectations of these stakeholders are multidimensional and vary considerably, and the concerns and expectations of different stakeholders have complex relationships. Implications for a retirement village development are also proposed. The findings of the study serve as a valuable reference for developers' understanding of stakeholder concerns and expectations, and for taking corresponding actions in the final stages of the retirement village development.

Overfitting Bayesian mixture models with an unknown number of components

This paper proposes solutions to three issues pertaining to the estimation of finite mixture models with an unknown number of components: the non-identifiability induced by overfitting the number of components, the mixing limitations of standard Markov Chain Monte Carlo (MCMC) sampling techniques, and the related label switching problem. An overfitting approach is used to estimate the number of components in a finite mixture model via a Zmix algorithm. Zmix provides a bridge between multidimensional samplers and test based estimation methods, whereby priors are chosen to encourage extra groups to have weights approaching zero. MCMC sampling is made possible by the implementation of prior parallel tempering, an extension of parallel tempering. Zmix can accurately estimate the number of components, posterior parameter estimates and allocation probabilities given a sufficiently large sample size. The results will reflect uncertainty in the final model and will report the range of possible candidate models and their respective estimated probabilities from a single run. Label switching is resolved with a computationally light-weight method, Zswitch, developed for overfitted mixtures by exploiting the intuitiveness of allocation-based relabelling algorithms and the precision of label-invariant loss functions. Four simulation studies are included to illustrate Zmix and Zswitch, as well as three case studies from the literature. All methods are available as part of the R package Zmix, which can currently be applied to univariate Gaussian mixture models.

Closed-form solutions for low-rank non-rigid reconstruction

Recovering the motion of a non-rigid body from a set of monocular images permits the analysis of dynamic scenes in uncontrolled environments. However, the extension of factorisation algorithms for rigid structure from motion to the low-rank non-rigid case has proved challenging. This stems from the comparatively hard problem of finding a linear “corrective transform” which recovers the projection and structure matrices from an ambiguous factorisation. We elucidate that this greater difficulty is due to the need to find multiple solutions to a non-trivial problem, casting a number of previous approaches as alleviating this issue by either a) introducing constraints on the basis, making the problems nonidentical, or b) incorporating heuristics to encourage a diverse set of solutions, making the problems inter-dependent. While it has previously been recognised that finding a single solution to this problem is sufficient to estimate cameras, we show that it is possible to bootstrap this partial solution to find the complete transform in closed-form. However, we acknowledge that our method minimises an algebraic error and is thus inherently sensitive to deviation from the low-rank model. We compare our closed-form solution for non-rigid structure with known cameras to the closed-form solution of Dai et al. [1], which we find to produce only coplanar reconstructions. We therefore make the recommendation that 3D reconstruction error always be measured relative to a trivial reconstruction such as a planar one.

Exact calculations of survival probability for diffusion on growing lines, disks, and spheres: The role of dimension

Unlike standard applications of transport theory, the transport of molecules and cells during embryonic development often takes place within growing multidimensional tissues. In this work, we consider a model of diffusion on uniformly growing lines, disks, and spheres. An exact solution of the partial differential equation governing the diffusion of a population of individuals on the growing domain is derived. Using this solution, we study the survival probability, S(t). For the standard nongrowing case with an absorbing boundary, we observe that S(t) decays to zero in the long time limit. In contrast, when the domain grows linearly or exponentially with time, we show that S(t) decays to a constant, positive value, indicating that a proportion of the diffusing substance remains on the growing domain indefinitely. Comparing S(t) for diffusion on lines, disks, and spheres indicates that there are minimal differences in S(t) in the limit of zero growth and minimal differences in S(t) in the limit of fast growth. In contrast, for intermediate growth rates, we observe modest differences in S(t) between different geometries. These differences can be quantified by evaluating the exact expressions derived and presented here.

Effects of self-reported wisdom on happiness: Not much more than emotional intelligence?

Wisdom and emotional intelligence are increasingly popular topics among happiness scholars. Despite their conceptual overlap, no empirical research has examined their interrelations and incremental predictive validities. The aims of this study were (a) to investigate associations between multidimensional conceptualizations of self-reported wisdom (Ardelt in Res Aging 25(3):275-324, 2003, 2004) and emotional intelligence (Davies et al. in J Pers Soc Psychol 75:989-1015, 1998) and (b) to examine the joint effects of self-reported wisdom and emotional intelligence on dimensions of happiness (life satisfaction as well as positive and negative affect). Data were provided by two samples: 175 university students and 400 online workers. Correlations between a composite wisdom score, a composite emotional intelligence score, and happiness facets were positive and moderate in size. Regression analyses showed that the effects of composite wisdom on life satisfaction and positive affect (but not negative affect) became weaker and non-significant when composite emotional intelligence was controlled. Additional analyses including three dimensions of the self-reported wisdom (cognitive, reflective, and affective wisdom) and four dimensions of emotional intelligence (self- and others-emotions appraisal, use and regulation of emotion) revealed a more differentiated pattern of results. Implications for future research on wisdom and happiness are discussed.

Information sharing in distance collaboration: A software engineering perspective

Information sharing in distance collaboration: A software engineering perspective, QueenslandFactors in software engineering workgroups such as geographical dispersion and background discipline can be conceptually characterized as "distances", and they are obstructive to team collaboration and information sharing. This thesis focuses on information sharing across multidimensional distances and develops an information sharing distance model, with six core dimensions: geography, time zone, organization, multi-discipline, heterogeneous roles, and varying project tenure. The research suggests that the effectiveness of workgroups may be improved through mindful conducts of information sharing, especially proactive consideration of, and explicit adjustment for, the distances of the recipient when sharing information.

A new family of multivariate heavy-tailed distributions with variable marginal amounts of tailweight: Application to robust clustering

We propose a family of multivariate heavy-tailed distributions that allow variable marginal amounts of tailweight. The originality comes from introducing multidimensional instead of univariate scale variables for the mixture of scaled Gaussian family of distributions. In contrast to most existing approaches, the derived distributions can account for a variety of shapes and have a simple tractable form with a closed-form probability density function whatever the dimension. We examine a number of properties of these distributions and illustrate them in the particular case of Pearson type VII and t tails. For these latter cases, we provide maximum likelihood estimation of the parameters and illustrate their modelling flexibility on simulated and real data clustering examples.

Which patients with chronic kidney disease have the greatest symptom burden? A comparative study of advanced CKD stage and dialysis modality

Background Chronic kidney disease (CKD) leads to a range of symptoms, which are often under-recognised and little is known about the multidimensional symptom experience in advanced CKD. Objectives To examine (1) symptom burden at CKD stages 4 and 5, and dialysis modalities, and (2) demographic and renal history correlates of symptom burden. Methods Using a cross-sectional design, a convenience sample of 436 people with CKD was recruited from three hospitals. The CKD Symptom Burden Index (CKD-SBI) was used to measure the prevalence, severity, distress and frequency of 32 symptoms. Demographic and renal history data were also collected. Results Of the sample, 75.5 % were receiving dialysis (haemodialysis, n = 287; peritoneal dialysis, n = 42) and 24.5 % were not undergoing dialysis (stage 4, n = 69; stage 5, n = 38). Participants reported an average of 13.01 ± 7.67 symptoms. Fatigue and pain were common and burdensome across all symptom dimensions. While approximately one-third experienced sexual symptoms, when reported these symptoms were frequent, severe and distressing. Haemodialysis, older age and being female were independently associated with greater symptom burden. Conclusions In CKD, symptom burden is better understood when capturing the multidimensional aspects of a range of physical and psychological symptoms. Fatigue, pain and sexual dysfunction are key contributors to symptom burden, and these symptoms are often under-recognised and warrant routine assessment. The CKD-SBI offers a valuable tool for renal clinicians to assess symptom burden, leading to the commencement of timely and appropriate interventions.

The nexus between poverty and deprivation in Vietnam

Unlike previous studies’ finding on western and developed economies, income is a significant determinant of multidimensional deprivation in Vietnam. This first study on a developing country also incorporates food security in a latent class framework to compute a new multidimensional deprivation index. It was found that chronic poverty and not transient poverty has a detrimental effect on multidimensional deprivation and thus current poverty alleviation programs should potentially be tailored according to these poverty types to effectively combat multidimensional deprivation. The finding that 20% of non-poor are most deprived with85% of this group living in urban Vietnam also points to the need for a new form of targeted policy.

R1SVM: A randomised nonlinear approach to large-scale anomaly detection

The problem of unsupervised anomaly detection arises in a wide variety of practical applications. While one-class support vector machines have demonstrated their effectiveness as an anomaly detection technique, their ability to model large datasets is limited due to their memory and time complexity for training. To address this issue for supervised learning of kernel machines, there has been growing interest in random projection methods as an alternative to the computationally expensive problems of kernel matrix construction and sup-port vector optimisation. In this paper we leverage the theory of nonlinear random projections and propose the Randomised One-class SVM (R1SVM), which is an efficient and scalable anomaly detection technique that can be trained on large-scale datasets. Our empirical analysis on several real-life and synthetic datasets shows that our randomised 1SVM algorithm achieves comparable or better accuracy to deep auto encoder and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.