Children's content regulation and the 'obesity epidemic'

Some 30 years ago, Australia introduced the Children's Television Standards (CTS) with the twin goals of providing children with high-quality local programs and offering some protection from the perceived harms of television. The most recent review of the CTS occurred in the context of a decade of increasing international concern at rising levels of overweight and obesity, especially in very young children. Overlapping regulatory jurisdictions and co-regulatory frameworks complicate the process of addressing pressing issues of child health, while rapid changes to the media ecology have both extended the amount of programming for children and increased the economic challenges for producers. Our article begins with an overview of the conceptual shifts in priorities articulated in the CTS over time. Using the 2007-09 Review of the CTS as a case study, it then examines the role of research and stakeholder discourses in the CTS review process and critiques the effectiveness of existing regulatory regimes, both in providing access to dedicated children's content and in addressing the problem of escalating obesity levels in the population.

A non-linear programming-based similarity reasoning scheme for modelling of monotonicity-preserving multi-input fuzzy inference systems

In this paper, the zero-order Sugeno Fuzzy Inference System (FIS) that preserves the monotonicity property is studied. The sufficient conditions for the zero-order Sugeno FIS model to satisfy the monotonicity property are exploited as a set of useful governing equations to facilitate the FIS modelling process. The sufficient conditions suggest a fuzzy partition (at the rule antecedent part) and a monotonically-ordered rule base (at the rule consequent part) that can preserve the monotonicity property. The investigation focuses on the use of two Similarity Reasoning (SR)-based methods, i.e., Analogical Reasoning (AR) and Fuzzy Rule Interpolation (FRI), to deduce each conclusion separately. It is shown that AR and FRI may not be a direct solution to modelling of a multi-input FIS model that fulfils the monotonicity property, owing to the difficulty in getting a set of monotonically-ordered conclusions. As such, a Non-Linear Programming (NLP)-based SR scheme for constructing a monotonicity-preserving multi-input FIS model is proposed. In the proposed scheme, AR or FRI is first used to predict the rule conclusion of each observation. Then, a search algorithm is adopted to look for a set of consequents with minimized root means square errors as compared with the predicted conclusions. A constraint imposed by the sufficient conditions is also included in the search process. Applicability of the proposed scheme to undertaking fuzzy Failure Mode and Effect Analysis (FMEA) tasks is demonstrated. The results indicate that the proposed NLP-based SR scheme is useful for preserving the monotonicity property for building a multi-input FIS model with an incomplete rule base.

Fuzzy ARTMAP and hybrid evolutionary programming for pattern classification

In this paper, an Evolutionary Artificial Neural Network (EANN) that combines the Fuzzy ARTMAP (FAM) network and a Hybrid Evolutionary Programming (HEP) model is introduced. The proposed FAM-HEP model, which combines the strengths of FAM and HEP, is able to construct its network structure autonomously as well as to perform learning and evolutionary search and adaptation concurrently. The effectiveness of the proposed FAM-HEP network is assessed empirically using several benchmark data sets and a real medical diagnosis problem. The performance of FAM-HEP is analyzed, and the results are compared with those of FAM-EP, FAM, and other classification models. In general, the results of FAM-HEP are better than those of FAM-EP and FAM, and are comparable with those from other classification models. The study also reveals the potential of FAM-HEP as an innovative EANN model for undertaking pattern classification problems in general, and a promising computerized decision support tool for tackling medical diagnosis tasks in particular.

The use of laptop computers in programming lectures

This research explores the effect of the use of laptop computers on students’ learning experiences during lectures. Our methodology involves embedding laptops with visualization software as a learning aid during lectures. We then employ a framework of seven principles of good practice in higher education to evaluate the impact of the use of laptop computers on the learning experience of computer programming students. Overall, we found that students were highly motivated and supportive of this innovative use of laptop computers with lectures.

Internet and distributed computing advancements : theoretical frameworks and practical applications

This book is a vital compendium of chapters on the latest research within the field of distributed computing, capturing trends in the design and development of Internet and distributed computing systems that leverage autonomic principles and techniques. The chapters provided within this collection offer a holistic approach for the development of systems that can adapt themselves to meet requirements of performance, fault tolerance, reliability, security, and Quality of Service (QoS) without manual intervention.

Using linear programming for weights identification of generalized bonferroni means in R

The generalized Bonferroni mean is able to capture some interaction effects between variables and model mandatory requirements. We present a number of weights identification algorithms we have developed in the R programming language in order to model data using the generalized Bonferroni mean subject to various preferences. We then compare its accuracy when fitting to the journal ranks dataset.

The impact structured patient assessment frameworks have on patient care: an integrative review

Aims and objectives:
To evaluate structured patient assessment frameworks' impact on patient care.

Background:
Accurate patient assessment is imperative to determine the status and needs of the patient and the delivery of appropriate patient care. Nurses must be highly skilled in conducting timely and accurate patient assessments to overcome environmental obstacles and deliver quality and safe patient care. A structured approach to patient assessment is widely accepted in everyday clinical practice, yet little is known about the impact structured patient assessment frameworks have on patient care.

Design:
Integrative review.

Methods:
An electronic database search was conducted using Cumulative Index to Nursing and Allied Health Literature, Medical Literature Analysis and Retrieval System, PubMed and ProQuest Dissertations and Theses. The reference sections of textbooks and journal articles on patient assessment were manually searched for further studies. A comprehensive peer review screening process was undertaken. Research studies were selected that evaluated the impact structured patient assessment frameworks have on patient care. Studies were included if frameworks were designed for use by paramedics, nurses or medical practitioners working in prehospital or acute in-hospital settings.

Results:
Twelve studies met the inclusion criteria. There were no studies that evaluate the impact of a generic nursing assessment framework on patient care. The use of a structured patient assessment framework improved clinician performance of patient assessment. Limited evidence was found to support other aspects of patient care including documentation, communication, care implementation, patient and clinician satisfaction, and patient outcomes.

Conclusion:
Structured patient assessment frameworks enhance clinician performance of patient assessment and hold the potential to improve patient care and outcomes; however, further research is required to address these evidence gaps, particularly in nursing.

Relevance to clinical practice:
Acute care clinicians should consider using structured patient assessment frameworks in clinical practice to enhance their performance of patient assessment.

Minimizing impact of bounded uncertainty on McNaughton's scheduling algorithm via interval programming

Uncertainty of data affects decision making process as it increases the risk and the costs of the decision. One of the challenges in minimizing the impact of the bounded uncertainty on any scheduling algorithm is the lack of information, as only the upper bound and the lower bound are provided without any known probability or membership function. On the contrary, probabilistic uncertainty can use probability distributions and fuzzy uncertainty can use the membership function. McNaughton's algorithm is used to find the optimum schedule that minimizes the makespan taking into consideration the preemption of tasks. The challenge here is the bounded inaccuracy of the input parameters for the algorithm, namely known as bounded uncertain data. This research uses interval programming to minimise the impact of bounded uncertainty of input parameters on McNaughton’s algorithm, it minimises the uncertainty of the cost function estimate and increase its optimality. This research is based on the hypothesis that doing the calculations on interval values then approximate the end result will produce more accurate results than approximating each interval input then doing numerical calculations.

The development of theoretical frameworks for understanding the learning of chemistry

Chemistry has unique characteristics that make it a difficult subject to understand including the abstract concepts, the three levels of representation of matter – the macroscopic level, the sub-microscopic level and the symbolic level, and the complexities concerning the representational and theoretical qualities and the reality of each level. Drawing on data from a study with first year university students learning introductory chemistry, this chapter looks at how these students’ understandings of the characteristics of chemistry influence the way they understand and learn chemistry. Two theoretical frameworks to describe how chemical concepts can be presented and understood are developed based on the research data: the expanding triangle and the rising iceberg. The aim of the frameworks are to further develop the ways of thinking about how students learn chemistry thereby developing a chemical epistemology – that is, an understanding of the knowledge of how chemical ideas are built and an understanding of the way of knowing about chemical processes. These two frameworks are proposed as useful tools for chemistry educators to better understand students learning, linking chemical education research to practice so as to inform pedagogical content knowledge. Chemical education research can be theoretical and is sometimes criticised for not impacting on teachers practice, so the pedagogy of chemistry teachers is discussed with the aim of exploring ways the two frameworks can be useful in developing teachers’ professional understandings of learning and teaching chemistry to promote changes in their practice and support student understandings.