Convergence's fundamental question

This paper asks the fundamental question of whether editorial managers and journalists are embracing convergence for business reasons or to do better journalism. Media organizations around the world are adopting various forms of convergence, and along the way embracing a range of business models. Several factors are influencing and driving the adoption of convergence—also known as multiple-platform publishing. Principal among them are the media's desire to reach as wide an audience as possible, consumers who want access to news in a variety of forms and times (news 24-7), and editorial managers' drive to cut costs. The availability of relatively cheap digital technology facilitates the convergence process. Many journalists believe that because that technology makes it relatively easy to convert and distribute any form of content into another, it is possible to produce new forms of storytelling and consequently do better journalism. This paper begins by defining convergence (as much as it is possible to do so) and describing the competing models. It then considers the environments that lead to easy introduction of convergence, followed by the factors that hinder it. Examples of converged media around the world are provided, and suggestions offered on how to introduce convergence. The paper concludes that successful convergence satisfies the twin aims of good journalism and good business practices.

Intersection of ideals : journalism, profits, technology and convergence

Journalism needs advertising and advertising needs journalism: advertising pays for good reporting just as good reporting attracts customers for advertising. Problems arise when the equation becomes unbalanced, such as during the recessions in the early part of the twenty-first century. This paper asks the key question of whether editorial managers and journalists are embracing convergence at this time for business reasons or to do better journalism. It begins from the perspective that media organisations around the world are adopting various forms of convergence, and along the way embracing a range of business models. Several factors are influencing and driving the adoption of convergence - also known as multiple-platform publishing. Principal among them are the media's desire to reach as wide an audience as possible, consumers who want access to news in a variety of forms and times (news 24/7), and editorial managers' drive to cut costs. The availability of relatively cheap digital technology facilitates the convergence process. Many journalists believe that because that technology makes it relatively easy to convert and distribute any form of content into another, it is possible to produce new forms of storytelling and consequently do better journalism. This paper begins by defining convergence (as much as it is possible to do so) and describing the key competing models. It then considers the environments that lead to easy introduction of convergence, followed by the factors that hinder it. Examples of converged media around the world are provided, and suggestions offered on how to introduce convergence. The paper concludes that successful convergence satisfies the twin aims of good journalism and good business practices.

Better journalism or better profits? A key convergence issue in an age of concentrated ownership

Convergence has become an accepted form of journalism at media organisations around the world. These organisations are adopting a range of business models to find ways to pay for these innovations. The main drivers behind this radical change in media production are consumers' changing media habits, cheaper digital technology, and the disruptive forces that these two drivers generate. Technology also makes possible new forms of storytelling, which potentially allows journalists the chance to do better journalism through convergence. This article focuses on the key issue of whether editorial managers and journalists are embracing convergence to save money, or to do better journalism. It begins by defining convergence (while accepting the wide variety of definitions) and describing two main models of implementation. It then considers the factors that lead to easy introduction of convergence followed by the factors that hinder its introduction. Examples are provided of converged media around the world. This article ends with a warning about the dangers for democracy of misapplied convergence in an era of increasing concentration of ownership.

A neural networks learning algorithm for minor component analysis and its convergence analysis

The eigenvector associated with the smallest eigenvalue of the autocorrelation matrix of input signals is called minor component. Minor component analysis (MCA) is a statistical approach for extracting minor component from input signals and has been applied in many fields of signal processing and data analysis. In this letter, we propose a neural networks learning algorithm for estimating adaptively minor component from input signals. Dynamics of the proposed algorithm are analyzed via a deterministic discrete time (DDT) method. Some sufficient conditions are obtained to guarantee convergence of the proposed algorithm.

Stability and convergence analysis of transform-domain LMS adaptive filters with second-order autoregressive process

In this paper, the stability and convergence properties of the class of transform-domain least mean square (LMS) adaptive filters with second-order autoregressive (AR) process are investigated. It is well known that this class of adaptive filters improve convergence property of the standard LMS adaptive filters by applying the fixed data-independent orthogonal transforms and power normalization. However, the convergence performance of this class of adaptive filters can be quite different for various input processes, and it has not been fully explored. In this paper, we first discuss the mean-square stability and steady-state performance of this class of adaptive filters. We then analyze the effects of the transforms and power normalization performed in the various adaptive filters for both first-order and second-order AR processes. We derive the input asymptotic eigenvalue distributions and make comparisons on their convergence performance. Finally, computer simulations on AR process as well as moving-average (MA) process and autoregressive-moving-average (ARMA) process are demonstrated for the support of the analytical results.

A comparison of methods to calculate the optimal load for maximal power output in the power clean

The aim of this study was to compare three calculation methods to determine the load that maximises power output in the power clean. Five male athletes (height=179.8 10.5cms, weight 91 .8 8.8kg, power dean 1RM = 117.0 20.5kg) performed two per cleans at 10% increments from 50% to 100% of 1RM. Bar displacement data was collected using a Ballistic Measurement System (BMS) and vertical ground reaction force (VGRF) data was measured by a Kistler 9287B Force Plate. Power output was calculated for BMS (system mass), BMS (bar mass) and VGRF/BMS system mass. Optimal load was determined to be 70% for the BMS (system mass) and VGRF BMS (system mass) methods and 90% for the BMS (bar mass) method. Sports scientists should be aware of the technical issues underlying these findings due to the practical ramifications for athlete testing and training.

Performances of three minimally invasive cardiac output monitoring systems

We evaluated cardiac output (CO) using three new methods – the auto-calibrated FloTrac–Vigileo (CO_ed), the non-calibrated Modelflow (CO_mf ) pulse contour method and the ultra-sound HemoSonic system (CO_hs) – with thermodilution (CO_td) as the reference. In 13 postoperative cardiac surgical patients, 104 paired CO values were assessed before, during and after four interventions: (i) an increase of tidal volume by 50%; (ii) a 10 cm H₂O increase in positive end-expiratory pressure; (iii) passive leg raising and (iv) head up position. With the pooled data the difference (bias (2SD)) between CO_ed and CO_td, CO_mf and CO_td and CO_hs and CO_td was 0.33 (0.90), 0.30 (0.69) and −0.41 (1.11) l.min⁻¹, respectively. Thus, Modelflow had the lowest mean squared error, suggesting that it had the best performance. CO_ed significantly overestimates changes in cardiac output while CO_mf and CO_hs values are not significantly different from those of CO_td. Directional changes in cardiac output by thermodilution were detected with a high score by all three methods.

Using input-output data in life cycle inventory analysis

The impacts on the environment from human activities are of increasing concern. The need to consider the reduction in energy consumption is of particular interest, especially in the construction and operation of buildings, which accounts for between 30 and 40% of Australia's national energy consumption. Much past and more recent emphasis has been placed on methods for reducing the energy consumed in the operation of buildings. With the energy embodied in these buildings having been shown to account for an equally large proportion of a building's life cycle energy consumption, there is a need to look at ways of reducing the embodied energy of buildings and related products. Life cycle assessment (LCA) is considered to be the most appropriate tool for assessing the life cycle energy consumption of buildings and their products. The life cycle inventory analysis (LCIA) step of a LCA, where an inventory of material and energy inputs is gathered, may currently suffer from several limitations, mainly concerned with the use of incomplete and unreliable data sources and LCIA methods. These traditional methods of LCIA include process-based and input-output-based LCIA. Process-based LCIA uses process specific data, whilst input-output-based LCIA uses data produced from an analysis of the flow of goods and services between sectors of the Australian economy, also known as input-output data. With the incompleteness and unreliability of these two respective methods in mind, hybrid LCIA methods have been developed to minimise the errors associated with traditional LCIA methods, combining both process and input-output data. Hybrid LCIA methods based on process data have shown to be incomplete. Hybrid LCIA methods based on input-output data involve substituting available process data into the input-output model minimising the errors associated with process-based hybrid LCIA methods. However, until now, this LCIA method had not been tested for its level of completeness and reliability. The aim of this study was to assess the reliability and completeness of hybrid life cycle inventory analysis, as applied to the Australian construction industry. A range of case studies were selected in order to apply the input-output-based hybrid LCIA method and evaluate the subsequent results as obtained from each case study. These case studies included buildings: two commercial office buildings, two residential buildings, a recreational building; and building related products: a solar hot water system, a building integrated photovoltaic system and a washing machine. The range of building types and products selected assisted in testing the input-output-based hybrid LCIA method for its applicability across a wide range of product types. The input-output-based hybrid LCIA method was applied to each of the selected case studies in order to obtain their respective embodied energy results. These results were then evaluated with the use of a number of evaluation methods. These evaluation methods included an analysis of the difference between the process-based and input-output-based hybrid LCIA results as an evaluation of the completeness of the process-based LCIA method. The second method of evaluation used was a comparison between equivalent process and input-output values used in the input-output-based hybrid LCIA method as a measure of reliability. It was found that the results from a typical process-based LCIA and process-based hybrid LCIA have a large gap when compared to input-output-based hybrid LCIA results (up to 80%). This gap has shown that the currently available quantity of process data in Australia is insufficient. The comparison between equivalent process-based and input-output-based LCIA values showed that the input-output data does not provide a reliable representation of the equivalent process values, for material energy intensities, material inputs and whole products. Therefore, the use of input-output data to account for inadequate or missing process data is not reliable. However, as there is currently no other method for filling the gaps in traditional process-based LCIA, and as input-output data is considered to be more complete than process data, and the errors may be somewhat lower, using input-output data to fill the gaps in traditional process-based LCIA appears to be better than not using any data at all. The input-output-based hybrid LCIA method evaluated in this study has shown to be the most sophisticated and complete currently available LCIA method for assessing the environmental impacts associated with buildings and building related products. This finding is significant as the construction and operation of buildings accounts for a large proportion of national energy consumption. The use of the input-output-based hybrid LCIA method for products other than those related to the Australian construction industry may be appropriate, especially if the material inputs of the product being assessed are similar to those typically used in the construction industry. The input-output-based hybrid LCIA method has been used to correct some of the errors and limitations associated with previous LCIA methods, without the introduction of any new errors. Improvements in current input-output models are also needed, particularly to account for the inclusion of capital equipment inputs (i.e. the energy required to manufacture the machinery and other equipment used in the production of building materials, products etc.). Although further improvements in the quantity of currently available process data are also needed, this study has shown that with the current available embodied energy data for LCIA, the input-output-based hybrid LCIA appears to provide the most reliable and complete method for use in assessing the environmental impacts of the Australian construction industry.