Computer attitude and classroom computers

Computers not only increase the speed and efficiency of our mental efforts, but in the process they also alter the problem-solving tasks we are faced with and, in so doing, they alter the cognitive processes we use to solve problems. Computers are fundamentally changing our forms of thinking (Colc & Griffin, 1980). Therefore, the computer should be seen as not only having the potential to amplify human mental capabilities, but also of providing a catalyst for intellectual development.

The Children's Depression Scale : assessment of factor structure with data from a normal population

A study investigated the reliability and construct validity of the Children's Depression Scale. The revised subscales were shown to have strong construct and face validity and high reliability.

Unusual hydrate stabilization in the two-dimensional layered structure of quinacrinium bis(2-carboxy-4,5-dichlorobenzoate) tetrahydrate, a proton-transfer compound of the drug quinacrine

The crystal structure of the hydrated proton-transfer compound of the drug quinacrine [rac-N'-(6-chloro-2-methoxyacridin-9-yl)-N,N-diethylpentane-1,4-diamine] with 4,5-dichlorophthalic acid, C23H32ClN3O2+ . 2(C8H3Cl2O4-).4H2O (I), has been determined at 200 K. The four labile water molecules of solvation form discrete ...O--H...O--H... hydrogen-bonded chains parallel to the quinacrine side chain, the two N--H groups of which act as hydrogen-bond donors for two of the water acceptor molecules. The other water molecules, as well as the acridinium H atom, also form hydrogen bonds with the two anion species and extend the structure into two-dimensional sheets. Between these sheets there are also weak cation--anion and anion--anion pi-pi aromatic ring interactions. This structure represents only the third example of a simple quinacrine derivative for which structural data are available but differs from the other two in that it is unstable in the X-ray beam due to efflorescence, probably associated with the destruction of the unusual four-membered water chain structures.

Enforcing P3P policies using a digital rights management system

The protection of privacy has gained considerable attention recently. In response to this, new privacy protection systems are being introduced. SITDRM is one such system that protects private data through the enforcement of licenses provided by consumers. Prior to supplying data, data owners are expected to construct a detailed license for the potential data users. A license specifies whom, under what conditions, may have what type of access to the protected data. The specification of a license by a data owner binds the enterprise data handling to the consumer’s privacy preferences. However, licenses are very detailed, may reveal the internal structure of the enterprise and need to be kept synchronous with the enterprise privacy policy. To deal with this, we employ the Platform for Privacy Preferences Language (P3P) to communicate enterprise privacy policies to consumers and enable them to easily construct data licenses. A P3P policy is more abstract than a license, allows data owners to specify the purposes for which data are being collected and directly reflects the privacy policy of an enterprise.

Clypeotheca, a new skeletal structure in scleractinian corals : a potential stress indicator

Physiological responses to environmental stress are increasingly well studied in scleractinian corals. This work reports a new stress-related skeletal structure we term clypeotheca. Clypeotheca was observed in several livecollected common reef-building coral genera and a two to three kya subfossil specimen from Heron Reef, Great Barrier Reef and consists of an epitheca-like skeletal wall that seals over the surface of parts of the corallum in areas of stress or damage. It appears to form from a coordinated process wherein neighboring polyps and adjoining coenosarc seal themselves off from the surrounding environment as they contract and die. Clypeotheca forms from inward skeletal centripetal growth at the edges of corallites and by the merging of flange-like outgrowths that surround individual spines over the surface of the coenosteum. Microstructurally, the merged flanges are similar to upsidedown dissepiments and true epitheca. Clypeotheca is interpreted primarily as a response to stress that may help protect the colony from invasion of unhealthy tissues by parasites or disease by retracting tissues in areas that have become unhealthy for the polyps. Identification of skeletal responses of corals to environmental stress may enable the frequency of certain types of environmental stress to be documented in past environments. Such data may be important for understanding the nature of reef dynamics through intervals of climate change and for monitoring the effects of possible anthropogenic stress in modern coral reef habitats.

Type extension and efficient AST manipulation

Oberon-2 is an object-oriented language with a class structure based on type extension. The runtime structure of Oberon-2 is described and the low-level mechanism for dynamic type checking explained. It is shown that the superior type-safety of the language, when used for programming styles based on heterogeneous, pointer-linked data structures, has an entirely negligible cost in runtime performance.

Prediction of adjacent vertebral fracture risk associated with vertebroplasty : a computer-based simulation study

Vertebrplasty involved injecting cement into a fractured vertebra to provide stabilisation. There is clinical evidence to suggest however that vertebroplasty may be assocated with a higher risk of adjacent vertebral fracture; which may be due to the change in material properties of the post-procedure vertebra modifying the transmission of mechanical stresses to adjacent vertebrae.

Comparison of 3D finite element analysis derived stiffness and BMD to determine the failure load of the excised proximal femur

Bone mineral density (BMD) is currently the preferred surrogate for bone strength in clinical practice. Finite element analysis (FEA) is a computer simulation technique that can predict the deformation of a structure when a load is applied, providing a measure of stiffness (N mm− 1). Finite element analysis of X-ray images (3D-FEXI) is a FEA technique whose analysis is derived from a single 2D radiographic image. This ex-vivo study demonstrates that 3D-FEXI derived from a conventional 2D radiographic image has the potential to significantly increase the accuracy of failure load assessment of the proximal femur compared with that currently achieved with BMD.

Using information criteria to select the correct variance–covariance structure for longitudinal data in ecology

1. Ecological data sets often use clustered measurements or use repeated sampling in a longitudinal design. Choosing the correct covariance structure is an important step in the analysis of such data, as the covariance describes the degree of similarity among the repeated observations. 2. Three methods for choosing the covariance are: the Akaike information criterion (AIC), the quasi-information criterion (QIC), and the deviance information criterion (DIC). We compared the methods using a simulation study and using a data set that explored effects of forest fragmentation on avian species richness over 15 years. 3. The overall success was 80.6% for the AIC, 29.4% for the QIC and 81.6% for the DIC. For the forest fragmentation study the AIC and DIC selected the unstructured covariance, whereas the QIC selected the simpler autoregressive covariance. Graphical diagnostics suggested that the unstructured covariance was probably correct. 4. We recommend using DIC for selecting the correct covariance structure.

XCFS - a novel approach for clustering XML documents using both the structure and the content

XML document clustering is essential for many document handling applications such as information storage, retrieval, integration and transformation. An XML clustering algorithm should process both the structural and the content information of XML documents in order to improve the accuracy and meaning of the clustering solution. However, the inclusion of both kinds of information in the clustering process results in a huge overhead for the underlying clustering algorithm because of the high dimensionality of the data. This paper introduces a novel approach that first determines the structural similarity in the form of frequent subtrees and then uses these frequent subtrees to represent the constrained content of the XML documents in order to determine the content similarity. The proposed method reduces the high dimensionality of input data by using only the structure-constrained content. The empirical analysis reveals that the proposed method can effectively cluster even very large XML datasets and outperform other existing methods.

Content, structure and orientations of the practice-led exegesis

The emergent field of practice-led research is a unique research paradigm that situates creative practice as both a driver and outcome of the research process. The exegesis that accompanies the creative practice in higher research degrees remains open to experimentation and discussion around what content should be included, how it should be structured, and its orientations. This paper contributes to this discussion by reporting on a content analysis of a large, local sample of exegeses. We have observed a broad pattern in contents and structure within this sample. Besides the introduction and conclusion, it has three main parts: situating concepts (conceptual definitions and theories), practical contexts (precedents in related practices), and new creations (the creative process, the artifacts produced and their value as research). This model appears to combine earlier approaches to the exegesis, which oscillated between academic objectivity in providing a context for the practice and personal reflection or commentary upon the creative practice. We argue that this hybrid or connective model assumes both orientations and so allows the researcher to effectively frame the practice as a research contribution to a wider field while doing justice to its invested poetics.

Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.