Green energy : sustainable energy sources and alternative technologies

Despite the general evolution and broadening of the scope of the concept of infrastructure in many other sectors, the energy sector has maintained the same narrow boundaries for over 80 years. Energy infrastructure is still generally restricted in meaning to the transmission and distribution networks of electricity and, to some extent, gas. This is especially true in the urban development context. This early 20th century system is struggling to meet community expectations that the industry itself created and fostered for many decades. The relentless growth in demand and changing political, economic and environmental challenges require a shift from the traditional ‘predict and provide’ approach to infrastructure which is no longer economically or environmentally viable. Market deregulation and a raft of demand and supply side management strategies have failed to curb society’s addiction to the commodity of electricity. None of these responses has addressed the fundamental problem. This chapter presents an argument for the need for a new paradigm. Going beyond peripheral energy efficiency measures and the substitution of fossil fuels with renewables, it outlines a new approach to the provision of energy services in the context of 21st century urban environments.

Development of a multi-scale finite element model of the osteoporotic lumbar vertebral body for the investigation of apparent level vertebra mechanics and micro-level trabecular mechanics.

Osteoporotic spinal fractures are a major concern in ageing Western societies. This study develops a multi-scale finite element (FE) model of the osteoporotic lumbar vertebral body to study the mechanics of vertebral compression fracture at both the apparent (whole vertebral body) and micro-structural (internal trabecular bone core)levels. Model predictions were verified against experimental data, and found to provide a reasonably good representation of the mechanics of the osteoporotic vertebral body. This novel modelling methodology will allow detailed investigation of how trabecular bone loss in osteoporosis affects vertebral stiffness and strength in the lumbar spine.

Body size and walking cadence affect lower extremity joint power in children's gait

Obese children move less and with greater difficulty than normal-weight counterparts but expend comparable energy. Increased metabolic costs have been attributed to poor biomechanics but few studies have investigated the influence of obesity on mechanical demands of gait. This study sought to assess three-dimensional lower extremity joint powers in two walking cadences in 28 obese and normal-weight children. 3D-motion analysis was conducted for five trials of barefoot walking at self-selected and 30% greater than self-selected cadences. Mechanical power was calculated at the hip, knee, and ankle in sagittal, frontal and transverse planes. Significant group differences were seen for all power phases in the sagittal plane, hip and knee power at weight acceptance and hip power at propulsion in the frontal plane, and knee power during mid-stance in the transverse plane. After adjusting for body weight, group differences existed in hip and knee power phases at weight acceptance in sagittal and frontal planes, respectively. Differences in cadence existed for all hip joint powers in the sagittal plane and frontal plane hip power at propulsion. Frontal plane knee power at weight acceptance and sagittal plane knee power at propulsion were significantly different between cadences. Larger joint powers in obese children contribute to difficulty performing locomotor tasks, potentially decreasing motivation to exercise.

Perception of affordances in multi-scale dynamics as an alternative explanation for equivalence of analogical and inferential reasoning in animals and humans

Recent claims of equivalence of animal and human reasoning are evaluated and a study of avian cognition serves as an exemplar of weaknesses in these arguments. It is argued that current research into neurobiological cognition lacks theoretical breadth to substantiate comparative analyses of cognitive function. Evaluation of a greater range of theoretical explanations is needed to verify claims of equivalence in animal and human cognition. We conclude by exemplifying how the notion of affordances in multi-scale dynamics can capture behavior attributed to processes of analogical and inferential reasoning in animals and humans.

Public accountability : the perceived usefulness of school annual reports

Annual reports are an important component of New Zealand schools’ public accountability. Through the annual report the governance body informs stakeholders about school aims, objectives, achievements, use of resources, and financial performance. This paper identifies the perceived usefulness of the school annual report to recipients and the extent to which it serves as an instrument of accountability and/or decision-usefulness. The study finds that the annual report is used for a variety of purposes, including: to determine if the school has conducted its activities effectively and achieved stated objectives and goals; to examine student achievements; to assess financial accountability and performance; and to make decisions about the school as a suitable environment for their child/children. Nevertheless, the study also finds that other forms of communication are more important sources of information about the school than the annual report which is seen to fall short of users’ required qualities of understandability, reliability and readability. It would appear imperative that policy makers review the functional role of the school annual report which is a costly document to prepare. Further, school managers need to engage in alternative means to communicate sufficient and meaningful information in the discharge of public accountability.

Alternative analytical methods for the identification of cancer-related symptom clusters

Advances in symptom management strategies through a better understanding of cancer symptom clusters depend on the identification of symptom clusters that are valid and reliable. The purpose of this exploratory research was to investigate alternative analytical approaches to identify symptom clusters for patients with cancer, using readily accessible statistical methods, and to justify which methods of identification may be appropriate for this context. Three studies were undertaken: (1) a systematic review of the literature, to identify analytical methods commonly used for symptom cluster identification for cancer patients; (2) a secondary data analysis to identify symptom clusters and compare alternative methods, as a guide to best practice approaches in cross-sectional studies; and (3) a secondary data analysis to investigate the stability of symptom clusters over time. The systematic literature review identified, in 10 years prior to March 2007, 13 cross-sectional studies implementing multivariate methods to identify cancer related symptom clusters. The methods commonly used to group symptoms were exploratory factor analysis, hierarchical cluster analysis and principal components analysis. Common factor analysis methods were recommended as the best practice cross-sectional methods for cancer symptom cluster identification. A comparison of alternative common factor analysis methods was conducted, in a secondary analysis of a sample of 219 ambulatory cancer patients with mixed diagnoses, assessed within one month of commencing chemotherapy treatment. Principal axis factoring, unweighted least squares and image factor analysis identified five consistent symptom clusters, based on patient self-reported distress ratings of 42 physical symptoms. Extraction of an additional cluster was necessary when using alpha factor analysis to determine clinically relevant symptom clusters. The recommended approaches for symptom cluster identification using nonmultivariate normal data were: principal axis factoring or unweighted least squares for factor extraction, followed by oblique rotation; and use of the scree plot and Minimum Average Partial procedure to determine the number of factors. In contrast to other studies which typically interpret pattern coefficients alone, in these studies symptom clusters were determined on the basis of structure coefficients. This approach was adopted for the stability of the results as structure coefficients are correlations between factors and symptoms unaffected by the correlations between factors. Symptoms could be associated with multiple clusters as a foundation for investigating potential interventions. The stability of these five symptom clusters was investigated in separate common factor analyses, 6 and 12 months after chemotherapy commenced. Five qualitatively consistent symptom clusters were identified over time (Musculoskeletal-discomforts/lethargy, Oral-discomforts, Gastrointestinaldiscomforts, Vasomotor-symptoms, Gastrointestinal-toxicities), but at 12 months two additional clusters were determined (Lethargy and Gastrointestinal/digestive symptoms). Future studies should include physical, psychological, and cognitive symptoms. Further investigation of the identified symptom clusters is required for validation, to examine causality, and potentially to suggest interventions for symptom management. Future studies should use longitudinal analyses to investigate change in symptom clusters, the influence of patient related factors, and the impact on outcomes (e.g., daily functioning) over time.

Analysis of distrubances in large interconnected power systems

World economies increasingly demand reliable and economical power supply and distribution. To achieve this aim the majority of power systems are becoming interconnected, with several power utilities supplying the one large network. One problem that occurs in a large interconnected power system is the regular occurrence of system disturbances which can result in the creation of intra-area oscillating modes. These modes can be regarded as the transient responses of the power system to excitation, which are generally characterised as decaying sinusoids. For a power system operating ideally these transient responses would ideally would have a “ring-down” time of 10-15 seconds. Sometimes equipment failures disturb the ideal operation of power systems and oscillating modes with ring-down times greater than 15 seconds arise. The larger settling times associated with such “poorly damped” modes cause substantial power flows between generation nodes, resulting in significant physical stresses on the power distribution system. If these modes are not just poorly damped but “negatively damped”, catastrophic failures of the system can occur. To ensure system stability and security of large power systems, the potentially dangerous oscillating modes generated from disturbances (such as equipment failure) must be quickly identified. The power utility must then apply appropriate damping control strategies. In power system monitoring there exist two facets of critical interest. The first is the estimation of modal parameters for a power system in normal, stable, operation. The second is the rapid detection of any substantial changes to this normal, stable operation (because of equipment breakdown for example). Most work to date has concentrated on the first of these two facets, i.e. on modal parameter estimation. Numerous modal parameter estimation techniques have been proposed and implemented, but all have limitations [1-13]. One of the key limitations of all existing parameter estimation methods is the fact that they require very long data records to provide accurate parameter estimates. This is a particularly significant problem after a sudden detrimental change in damping. One simply cannot afford to wait long enough to collect the large amounts of data required for existing parameter estimators. Motivated by this gap in the current body of knowledge and practice, the research reported in this thesis focuses heavily on rapid detection of changes (i.e. on the second facet mentioned above). This thesis reports on a number of new algorithms which can rapidly flag whether or not there has been a detrimental change to a stable operating system. It will be seen that the new algorithms enable sudden modal changes to be detected within quite short time frames (typically about 1 minute), using data from power systems in normal operation. The new methods reported in this thesis are summarised below. The Energy Based Detector (EBD): The rationale for this method is that the modal disturbance energy is greater for lightly damped modes than it is for heavily damped modes (because the latter decay more rapidly). Sudden changes in modal energy, then, imply sudden changes in modal damping. Because the method relies on data from power systems in normal operation, the modal disturbances are random. Accordingly, the disturbance energy is modelled as a random process (with the parameters of the model being determined from the power system under consideration). A threshold is then set based on the statistical model. The energy method is very simple to implement and is computationally efficient. It is, however, only able to determine whether or not a sudden modal deterioration has occurred; it cannot identify which mode has deteriorated. For this reason the method is particularly well suited to smaller interconnected power systems that involve only a single mode. Optimal Individual Mode Detector (OIMD): As discussed in the previous paragraph, the energy detector can only determine whether or not a change has occurred; it cannot flag which mode is responsible for the deterioration. The OIMD seeks to address this shortcoming. It uses optimal detection theory to test for sudden changes in individual modes. In practice, one can have an OIMD operating for all modes within a system, so that changes in any of the modes can be detected. Like the energy detector, the OIMD is based on a statistical model and a subsequently derived threshold test. The Kalman Innovation Detector (KID): This detector is an alternative to the OIMD. Unlike the OIMD, however, it does not explicitly monitor individual modes. Rather it relies on a key property of a Kalman filter, namely that the Kalman innovation (the difference between the estimated and observed outputs) is white as long as the Kalman filter model is valid. A Kalman filter model is set to represent a particular power system. If some event in the power system (such as equipment failure) causes a sudden change to the power system, the Kalman model will no longer be valid and the innovation will no longer be white. Furthermore, if there is a detrimental system change, the innovation spectrum will display strong peaks in the spectrum at frequency locations associated with changes. Hence the innovation spectrum can be monitored to both set-off an “alarm” when a change occurs and to identify which modal frequency has given rise to the change. The threshold for alarming is based on the simple Chi-Squared PDF for a normalised white noise spectrum [14, 15]. While the method can identify the mode which has deteriorated, it does not necessarily indicate whether there has been a frequency or damping change. The PPM discussed next can monitor frequency changes and so can provide some discrimination in this regard. The Polynomial Phase Method (PPM): In [16] the cubic phase (CP) function was introduced as a tool for revealing frequency related spectral changes. This thesis extends the cubic phase function to a generalised class of polynomial phase functions which can reveal frequency related spectral changes in power systems. A statistical analysis of the technique is performed. When applied to power system analysis, the PPM can provide knowledge of sudden shifts in frequency through both the new frequency estimate and the polynomial phase coefficient information. This knowledge can be then cross-referenced with other detection methods to provide improved detection benchmarks.