Nothing to gain, plenty to lose

Empirical research examining the proposed privatisation of the NSW electricity transmission and distribution businesses has found the asset recycling strategy is likely to negatively impact the state’s credit rating over the medium to long term. The analysis also examined the relative efficiency of public and private networks, finding that once the physical span of each network was considered — essentially the number of kilometres covered — publicly owned electricity networks currently operate more efficiently that privately owned assets in other states. Written by Market Economics managing director Stephen Koukoulas, who has more than 25 years experience as an economist in government and banking, the report found there was no logical case for privatisation, with many arguments based on questionable assumptions and generalisations. The report also highlights that the electricity transmission and distribution businesses currently provide a relatively stable and low-risk cash flow to the budget. The report made a number of key findings, including: • Privatising NSW’s transmission and distribution assets is likely to drive up prices due to higher overheads in comparable privatised businesses; • The physical span of different networks is the single largest factor behind variations in both operational and capital expenditure; • NSW’s publicly owned networks outperforms privately owned peers on operating expenses; • Publicly owned networks appear more willing to engage in long-term planning when undertaking capital expenditure; and • Privatising NSW’s electricity network assets offers little short term budgetary gain and could well be detrimental over the medium to long term

School Library Trends: A Bibliometric and Content Analysis

Today, AASL Twitter is one of the most widely used social media communications among school library practitioners. While scholarly communications in school library is conducted in an array of topics across the field of school library, it is difficult to establish how much of the scholarly communications is exposed to these practitioners. The study included three phases in its design: 1) To conduct a bibliometric analysis to find out the major authors, affiliations, themes and evolution of journals in field of school library, 2) To complete a content analysis of the AASL Twitter social media communications to find out the major participants, affiliations and themes and evolution in AASL Twitter communications, 3) To compare and contrast the major authors and themes of evolution in scholarly communications in the field of school library and AASL Twitter communications. During the years 1905-2018 scholarly communications have gone through various stages including infancy, growth and an upsurge stages. In recent years scholarly communications have been decreasing from the years 2010 to 2018. Trends in themes among scholarly communications and AASL Twitter communications include media, books, reading, Internet, children, literacy, standards, awards, technology, education, public, resources, teachers, students and electronic themes among other results. The trends between scholarly communications in the field of school library and AASL Twitter communications help provide support for future constructive goals among school library professionals

Optical characterisation of polymeric nanocomposites using tomographic, spectroscopic and Fraunhofer wavefront assessment

Polymers are often embedded with specific nanofillers such that the functional characteristics and properties of the resulting polymeric nanocomposite (PNC) are enhanced. The degree to which these enhancements can be achieved depends not only on the level of particle loading of nanofillers, but most importantly on the resulting dispersion profile achieved within the matrix. Agglomeration (often referred to as clustering) is a result of the mixing process and very much depends on the chemistry between the polymer and nanofiller. Depending on the PNC type, different mixing processes can be applied but the general consensus is that such processes are not repeatable themselves. Not only it is quite difficult to achieve the desired level of dispersion, but in addition there is a limited number of characterization tools that can be employed to routinely check the homogeneity achieved within a produced sample. Transmission electron microscopy (TEM) and X-ray diffraction (XRD) techniques are usually employed, but they are very time consuming, expensive, require special sample preparation and treatment, often produce results that are difficult to interpret and can only analyse very small areas of sample. This work reports on the adaptation and development and three optical techniques that are non-destructive, can accurately characterize the dispersion achieved as a result of the mixing process and can analyse larger material areas. The techniques reported are based on static and dynamic visible and infra-red light scattering

Clustering measure-valued data with Wasserstein barycenters

In this work, learning schemes for measure-valued data are proposed, i.e. data that their structure can be more efficiently represented as probability measures instead of points on $\R^d$, employing the concept of probability barycenters as defined with respect to the Wasserstein metric. Such type of learning approaches are highly appreciated in many fields where the observational/experimental error is significant (e.g. astronomy, biology, remote sensing, etc.) or the data nature is more complex and the traditional learning algorithms are not applicable or effective to treat them (e.g. network data, interval data, high frequency records, matrix data, etc.). Under this perspective, each observation is identified by an appropriate probability measure and the proposed statistical learning schemes rely on discrimination criteria that utilize the geometric structure of the space of probability measures through core techniques from the optimal transport theory. The discussed approaches are implemented in two real world applications: (a) clustering eurozone countries according to their observed government bond yield curves and (b) classifying the areas of a satellite image to certain land uses categories which is a standard task in remote sensing. In both case studies the results are particularly interesting and meaningful while the accuracy obtained is high.Comment: 18 pages, 3 figure

Penetration in the CNDO Theory

An explicit form for the penetration integrals in CNDO theory is developed and tested on first row diatomic molecules. It is shown that the non-penetration integral theory CNDO/BW is more satisfactory for predicting bond lengths and equilibrium energies because it gives an unstable 3 ~u+ for H2, whereas introduction of the penetration integrals makes this state stable

Multi-temporal Forest Cover Change and Forest Density Trend Detection in a Mediterranean Environment

The loss of forests along with the various types of shrubs in the Mediterranean region is seen as an important driver of climate change and has been repeatedly related with the observed land degradation and desertification in the region. Nevertheless, the extent of woody perennial vegetation cover (WPVC) and its density remain largely unclear. Here, we apply a series of algorithms and methods operationally used in Australia for large-scale WPVC mapping and monitoring and demonstrate their applicability in the Mediterranean region using a Spanish area as the trial site. Five Landsat TM and ETM+ images from various dates spanning 14 years are used to map changes in the extent of WPVC and to identify areas with a declining, stabilising or recovering trend. Results show that the applied methodology, which incorporates (i) preprocessing of the Landsat imagery, (ii) a canonical variate analysis to spectrally discriminate between woody and non-woody land cover types, (iii) a conditional probability network and (iv) spectral indices for mapping woody cover and density trend, is highly successful and well suited for use in Mediterranean environments. A rigorous accuracy assessment is undertaken producing overall accuracies above 97% for both woody and non-woody cover types and all dates. Results also show that in the area of study, the majority of WPVC disturbances were due to forest fires, which represent the region's most frequent natural and anthropogenic disturbance. This raises significant concerns about the future of the area's WPVC. Regeneration compensated to some degree for the high disturbance rates. Copyright © 2015 John Wiley & Sons, Ltd

A Random Forest-Cellular Automata modelling approach to explore future land use/cover change in Attica (Greece), under different socio-economic realities and scales

This paper explores potential future land use/cover (LUC) dynamics in the Attica region, Greece, under three distinct economic performance scenarios. During the last decades, Attica underwent a significant and predominantly unregulated process of urban growth, due to a substantial increase in housing demand coupled with limited land use planning controls. However, the recent financial crisis affected urban growth trends considerably. This paper uses the observed LUC trends between 1991 and 2016 to sketch three divergent future scenarios of economic development. The observed LUC trends are then analysed using 27 dynamic, biophysical, socio-economic, terrain and proximity-based factors, to generate transition potential maps, implementing a Random Forests (RF) regression modelling approach. Scenarios are projected to 2040 by implementing a spatially explicit Cellular Automata (CA) model. The resulting maps are subjected to a multiple resolution sensitivity analysis to assess the effect of spatial resolution of the input data to the model outputs. Findings show that, under the current setting of an underdeveloped land use planning apparatus, a long-term scenario of high economic growth will increase built-up surfaces in the region by almost 24%, accompanied by a notable decrease in natural areas and cropland. Interestingly, in the case that the currently negative economic growth rates persist, artificial surfaces in the region are still expected to increase by approximately 7.5% by 2040

Multi-temporal land-cover classification and change analysis with conditional probability networks: The case of Lesvos Island (Greece)

This study uses a series of Landsat images to map the main land-cover types on the Mediterranean island of Lesvos, Greece. We compare a single-year maximum likelihood classification (MLC) with a multi-temporal maximum likelihood classification (MTMLC) approach, with time-series class labels modelled using a first-order hidden Markov model comprising continuous and discrete variables. A rigorous validation scheme shows statistically significant higher accuracy figures for the multi-temporal approach. Land-cover change accuracies were also greatly improved by the proposed methodology: from 46% to 70%. The results show that when only two dates are used, the mapping of land use/cover is unreliable and a large number of the changes identified are due to the individual-year commission and omission errors