Measuring Australia's well-being using hierarchical needs

A new approach to well-being measurement is presented in this paper based on multidimensional hierarchical human needs and motivation. This paper empirically applies this new measure of well-being to Australia for the period 1985–2000. This hierarchical approach is underpinned by a rigorous psychological theory of human motivation. Hierarchical human needs are classified into five categories. Eight indicators have been chosen to reflect these categories. A composite indicator of these eight indicators is calculated. This paper concludes that it is necessary to consider multidimensional human needs and motivation when analysing and seeking to improve well-being through economic and social development activities.

Phase behavior, crystallization, and hierarchical nanostructures in self-organized thermoset blends of epoxy resin and amphiphilic poly(ethylene oxide)-block-poly(propylene oxide)-block-poly(ethylene oxide) triblock copolymers

Nanostructured thermoset blends of bisphenol A-type epoxy resin (ER) and amphiphilic poly(ethylene oxide)-block-poly(propylene oxide)-block-poly(ethylene oxide) (PEO-PPO-PEO) triblock copolymers were successfully prepared. Two samples of PEO-PPO-PEO triblock copolymer with different ethylene oxide (EO) contents, denoted as EO30 with 30 wt % EO content and EO80 with 80 wt % EO content, were used to form the self-organized thermoset blends of varying compositions using 4,4'-methylenedianiline (MDA) as curing agent. The phase behavior, crystallization, and morphology were investigated by differential scanning calorimetry (DSC), transmission electron microscopy (TEM), atomic force microscopy (AFM), and small-angle X-ray scattering (SAXS). It was found that macroscopic phase separation took place in the MDA-cured ER/EO30 blends containing 60-80 wt % EO30 triblock copolymer. The MDA-cured ER/EO30 blends with EO30 content up to 50 wt % do not show macroscopic phase separation but exhibit nanostructures on the order of 10-30 nm as revealed by both the TEM and SAXS studies. The AFM study further shows that the ER/EO30 blend at some composition displays structural inhomogeneity at two different nanoscales and is hierarchically nanostructured. The spherical PPO domains with an average size of about 10 nm are uniformly dispersed in the 80/20 ER/EO30 blend; meanwhile, a structural inhomogeneity on the order of 50-200 nm is observed. The ER/EO80 blends are not macroscopically phase-separated over the entire composition range because of the much higher PEO content of the EO80 triblock copolymer. However, the ER/EO80 blends show composition-dependent nanostructures on the order of 10-100 nm. The 80/20 ER/EO80 blend displays hierarchical structures at two different nanoscales, i.e., a bicontinuous microphase structure on the order of about 100 nm and spherical domains of 10-20 nm in diameter uniformly dispersed in both the continuous microphases. The blends with 60 wt % and higher EO80 content are completely volume-filled with spherulites. Bundles of PEO lamellae with spacing of 20-30 nm interwoven with a microphase structure on the order of about 100 nm are revealed by AFM study for the 30/70 ER/EO80 blend.

Web page clustering : a hyperlink-based similarity and matrix-based hierarchical algorithms

This paper proposes a hyperlink-based web page similarity measurement and two matrix-based hierarchical web page clustering algorithms. The web page similarity measurement incorporates hyperlink transitivity and page importance within the concerned web page space. One clustering algorithm takes cluster overlapping into account, another one does not. These algorithxms do not require predefined similarity thresholds for clustering, and are independent of the page order. The primary evaluations show the effectiveness of the proposed algorithms in clustering improvement.

Interpersonal trust and formal controls in hierarchical relationships

This paper examines the influence of the level of interpersonal trust between superior and subordinate managers on the control behaviour of the former. On the basis of a questionnaire survey and interviews of senior managers from business organisations in Sri Lanka, and a survey of managers in Beijing-China the study explores the control behaviour of superior managers when their trust in a subordinate is high or low. Sri Lanka and China, societies in which the dependence on interpersonal trust is believed to be high, were chosen for the study to maximise the effect of interpersonal trust.

The findings of this study indicate that a superior’s high (low) trust in a subordinate is associated with a low (high) level of monitoring and a high (low) level of social interactions. The hypothesis that a superior’s high (low) level of trust is associated with a low (high) level of reliance on formal control was supported only in the Sri Lankan sample. These findings are at least indicative of control behaviour of superior managers in Sri Lanka and China and possibly of other countries in Asia. An understanding of the trust-sensitive control behaviour of managers in this region is particularly important for designing and implementing effective control systems for international organizations operating in the region.

Hierarchical relationship between values, lifestyles, possessions and food consumption among Beijing adult population

A representative sample (N=801) of the Beijing adult population was used to empirically test and validate a proposed hierarchical model of relationships between values, lifestyles, possessions and food consumption. The theoretical contribution of the present study is the development and an empirically supported paradigm, which explains consumers' consumption behaviour. A further outcome is the substantiation of a pathway model involving hierarchical relationship between values, lifestyle, and demographic variables on material possessions and food consumption.

Selective hydrogen bonding and hierarchical nanostructures in poly(hydroxyether of bisphenol A)/poly(ε-caprolactone)-block-poly(2-vinyl pyridine) blends

The phase behavior, hydrogen bonding interactions and morphology of poly(hydroxyether of bisphenol A) (phenoxy) and poly(var epsilon-caprolactone)-block-poly(2-vinyl pyridine) (PCL-b-P2VP) were investigated using differential scanning calorimetry (DSC), Fourier transform infrared (FTIR) spectroscopy, optical microscopy and atomic force microscopy (AFM). In this A-b-B/C type block copolymer/homopolymer system, both P2VP and PCL blocks have favorable intermolecular interaction towards phenoxy via hydrogen bonding. However, the hydrogen bonding between P2VP and phenoxy is significantly stronger than that between PCL and phenoxy. Selective hydrogen bonding between phenoxy/P2VP pair at lower phenoxy contents and co-existence of two competitive hydrogen bonding interactions between phenoxy/P2VP and phenoxy/PCL pairs at higher phenoxy contents were observed in the blends. This leads to the formation of a variety of composition dependent nanostructures including wormlike, hierarchical and core–shell morphologies. The blends became homogeneous at 95 wt% phenoxy where both blocks of the PCL-b-P2VP were miscible with phenoxy due to hydrogen bonding. In the end, a model was proposed to explain the microphase morphology of blends based on the experimental results obtained. The swelling of the PCL-b-P2VP block copolymer by phenoxy due to selective hydrogen bonding causes formation of different microphases

Superhydrophobic polyimide films with a hierarchical topography : combined replica molding and layer-by-layer assembly

Artificial superhydrophobic surfaces with a hierarchical topography were fabricated by using layer-by-layer assembly of polyelectrolytes and silica nanoparticles on microsphere-patterned polyimide precursor substrates followed with thermal and fluoroalkylsilane treatment. In this special hierarchical topography, micrometer-scale structures were provided by replica molding of polyamic acid using two-dimensional arrays of polystyrene latex spheres as templates, and nanosized silica particles were then assembled on these microspheres to construct finer structures at the nanoscale. Heat treatment was conducted to induce chemical cross-linking between polyelectrolytes and simultaneously convert polyamic acid to polyimide. After surface modification with fluoroalkylsilane, the as-prepared highly hydrophilic surface was endowed with superhydrophobicity due to the bioinspired combination of low surface energy materials and hierarchical surface structures. A superhydrophobic surface with a static water contact angle of 160 degrees and sliding angle of less than 10 degrees was obtained. Notably, the polyimide microspheres were integrated with the substrate and were mechanically stable. In addition, the chemical and mechanical stability of the polyelectrolyte/silica nanoparticle multilayers could be increased by heat-induced cross-linking between polyelectrolytes to form nylon-like films, as well as the formation of interfacial chemical bonds.

Hierarchical development of training database for artificial neural network-based damage identification

Though serving as an effective means for damage identification, the capability of an artificial neural network (ANN) for quantitative prediction is substantially dependent on the amount of training data. In virtue of a concept of “Digital Damage Fingerprints” (DDF), a hierarchical approach for the development of training databases was proposed for ANN-based damage identification. With the object of exploiting the capability of ANN to address the key questions: “Is there damage?” and “Where is the damage?”, the amount of training data (damage cases) was increased progressively. Mutuality was established between the quantity of training data and the accuracy of answers to the two questions of interest, and was experimentally validated by identifying the position of actual damage in carbon fibre-reinforced composite laminates. The results demonstrate that such a hierarchical approach is capable of offering prediction as to the presence and location of damage individually, with substantially reduced computational cost and effort in the development of the ANN training database.

Adaptive hierarchical scheduling policy for enterprise grid computing systems

In an enterprise grid computing environments, users have access to multiple resources that may be distributed geographically. Thus, resource allocation and scheduling is a fundamental issue in achieving high performance on enterprise grid computing. Most of current job scheduling systems for enterprise grid computing provide batch queuing support and focused solely on the allocation of processors to jobs. However, since I/O is also a critical resource for many jobs, the allocation of processor and I/O resources must be coordinated to allow the system to operate most effectively. To this end, we present a hierarchical scheduling policy paying special attention to I/O and service-demands of parallel jobs in homogeneous and heterogeneous systems with background workload. The performance of the proposed scheduling policy is studied under various system and workload parameters through simulation. We also compare performance of the proposed policy with a static space–time sharing policy. The results show that the proposed policy performs substantially better than the static space–time sharing policy.

Mining multi-modal crime patterns at different levels of granularity using hierarchical clustering

The appearance of patterns could be found in different modalities of a domain, where the different modalities refer to the data sources that constitute different aspects of a domain. Particularly, the domain of our discussion refers to crime and the different modalities refer to the different data sources such as offender data, weapon data, etc. in crime domain. In addition, patterns also exist in different levels of granularity for each modality. In order to have a thorough understanding a domain, it is important to reveal the hidden patterns through the data explorations at different levels of granularity and for each modality. Therefore, this paper presents a new model for identifying patterns that exist in different levels of granularity for different modes of crime data. A hierarchical clustering approach - growing self organising maps (GSOM) has been deployed. Furthermore, the model is enhanced with experiments that exhibit the significance of exploring data at different granularities.

Procedural modeling facilities for hierarchical object generation

We modify a selection of interactive modeling tools for use in a procedural modeling environment. These tools are selection, extrusion, subdivision and curve shaping. We create human models to demonstrate that these tools are appropriate for use on hierarchical objects. Our tools support the main benefits of procedural modeling, which are: the use of parameterisation to control and very a model, varying levels of detail, increased model complexity, base shape independence and database amplification. We demonstrate scripts which provide each of these benefits.

Hierarchical rule generalisation for speaker identification in fiction books

This paper presents a hierarchical pattern matching and generalisation technique which is applied to the problem of locating the correct speaker of quoted speech found in fiction books. Patterns from a training set are generalised to create a small number of rules, which can be used to identify items of interest within the text. The pattern matching technique is applied to finding the Speech-Verb, Actor and Speaker of quotes found in ction books. The technique performs well over the training data, resulting in rule-sets many times smaller than the training set, but providing very high accuracy. While the rule-set generalised from one book is less effective when applied to different books than an approach based on hand coded heuristics, performance is comparable when testing on data closely related to the training set.