Spatially explicit structural equation modeling

Structural equation modeling (SEM) is a powerful statistical approach for the testing of networks of direct and indirect theoretical causal relationships in complex data sets with intercorrelated dependent and independent variables. SEM is commonly applied in ecology, but the spatial information commonly found in ecological data remains difficult to model in a SEM framework. Here we propose a simple method for spatially explicit SEM (SE-SEM) based on the analysis of variance/covariance matrices calculated across a range of lag distances. This method provides readily interpretable plots of the change in path coefficients across scale and can be implemented using any standard SEM software package. We demonstrate the application of this method using three studies examining the relationships between environmental factors, plant community structure, nitrogen fixation, and plant competition. By design, these data sets had a spatial component, but were previously analyzed using standard SEM models. Using these data sets, we demonstrate the application of SE-SEM to regularly spaced, irregularly spaced, and ad hoc spatial sampling designs and discuss the increased inferential capability of this approach compared with standard SEM. We provide an R package, sesem, to easily implement spatial structural equation modeling.

Frequency response function based damage identification using principal component analysis and pattern recognition technique

Pattern recognition is a promising approach for the identification of structural damage using measured dynamic data. Much of the research on pattern recognition has employed artificial neural networks (ANNs) and genetic algorithms as systematic ways of matching pattern features. The selection of a damage-sensitive and noise-insensitive pattern feature is important for all structural damage identification methods. Accordingly, a neural networks-based damage detection method using frequency response function (FRF) data is presented in this paper. This method can effectively consider uncertainties of measured data from which training patterns are generated. The proposed method reduces the dimension of the initial FRF data and transforms it into new damage indices and employs an ANN method for the actual damage localization and quantification using recognized damage patterns from the algorithm. In civil engineering applications, the measurement of dynamic response under field conditions always contains noise components from environmental factors. In order to evaluate the performance of the proposed strategy with noise polluted data, noise contaminated measurements are also introduced to the proposed algorithm. ANNs with optimal architecture give minimum training and testing errors and provide precise damage detection results. In order to maximize damage detection results, the optimal architecture of ANN is identified by defining the number of hidden layers and the number of neurons per hidden layer by a trial and error method. In real testing, the number of measurement points and the measurement locations to obtain the structure response are critical for damage detection. Therefore, optimal sensor placement to improve damage identification is also investigated herein. A finite element model of a two storey framed structure is used to train the neural network. It shows accurate performance and gives low error with simulated and noise-contaminated data for single and multiple damage cases. As a result, the proposed method can be used for structural health monitoring and damage detection, particularly for cases where the measurement data is very large. Furthermore, it is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy under varying levels of damage.

A framework for evaluating the impact of obesity prevention strategies on socioeconomic inequalities in weight

We developed a theoretical framework to organize obesity prevention interventions by their likely impact on the socioeconomic gradient of weight. The degree to which an intervention involves individual agency versus structural change influences socioeconomic inequalities in weight. Agentic interventions, such as standalone social marketing, increase socioeconomic inequalities. Structural interventions, such as food procurement policies and restrictions on unhealthy foods in schools, show equal or greater benefit for lower socioeconomic groups. Many obesity prevention interventions belong to the agento–structural types of interventions, and account for the environment in which health behaviors occur, but they require a level of individual agency for behavioral change, including workplace design to encourage exercise and fiscal regulation of unhealthy foods or beverages. Obesity prevention interventions differ in their effectiveness across socioeconomic groups. Limiting further increases in socioeconomic inequalities in obesity requires implementation of structural interventions. Further empirical evaluation, especially of agento–structural type interventions, remains crucial.

The correlation between reading and mathematics ability at age twelve has a substantial genetic component

Dissecting how genetic and environmental influences impact on learning is helpful for maximizing numeracy and literacy. Here we show, using twin and genome-wide analysis, that there is a substantial genetic component to children’s ability in reading and mathematics, and estimate that around one half of the observed correlation in these traits is due to shared genetic effects (so-called Generalist Genes). Thus, our results highlight the potential role of the learning environment in contributing to differences in a child’s cognitive abilities at age twelve.

Evaluation framework to assess orthopaedic procedures

Introduction & aims The demand for evidence of efficacy of treatments in general and orthopaedic surgical procedures in particular is ever increasing in Australia and worldwide. The aim of this study is to share the key elements of an evaluation framework recently implemented in Australia to determine the efficacy of bone-anchored prostheses. Method The proposed evaluation framework to determine the benefit and harms of bone-anchored prostheses for individuals with limb loss was extracted from a systematic review of the literature including seminal studies focusing on clinical benefits and safety of procedures involving screw-type implant (e.g., OPRA) and press-fit fixations (e.g., EEFT, ILP, OPL). [1-64] Results The literature review highlighted that a standard and replicable evaluation framework should focus on: • The clinical benefits with a systematic recording of health-related quality of life (e.g., SF-26, Q-TFA), mobility predictor (e.g., AMPRO), ambulation abilities (e.g., TUG, 6MWT), walking abilities (e.g., characteristic spatio-temporal) and actual activity level at baseline and follow-up post Stage 2 surgery, • The potential harms with systematic recording of residuum care, infection, implant stability, implant integrity, injuries (e.g., falls) after Stage 1 surgery. There was a general consensus around the instruments to monitor most of the benefits and harms. The benefits could be assessed using a wide spectrum of complementary assessments ranging from subjective patient self-reporting to objective measurements of physical activity. However, this latter was assessed using a broad range of measurements (e.g., pedometer, load cell, energy consumption). More importantly, the lack of consistent grading of infections was sufficiently noticeable to impede cross-fixation comparisons. Clearly, a more universal grading system is needed. Conclusions Investigators are encouraged to implement an evaluation framework featuring the domains and instruments proposed above using a single database to facilitate robust prospective studies about potential benefits and harms of their procedure. This work is also a milestone in the development of national and international clinical outcome registries.

Analysis of differential gene expression based on Bayesian estimation of variance

Gene expression is arguably the most important indicator of biological function. Thus identifying differentially expressed genes is one of the main aims of high throughout studies that use microarray and RNAseq platforms to study deregulated cellular pathways. There are many tools for analysing differentia gene expression from transciptomic datasets. The major challenge of this topic is to estimate gene expression variance due to the high amount of ‘background noise’ that is generated from biological equipment and the lack of biological replicates. Bayesian inference has been widely used in the bioinformatics field. In this work, we reveal that the prior knowledge employed in the Bayesian framework also helps to improve the accuracy of differential gene expression analysis when using a small number of replicates. We have developed a differential analysis tool that uses Bayesian estimation of the variance of gene expression for use with small numbers of biological replicates. Our method is more consistent when compared to the widely used cyber-t tool that successfully introduced the Bayesian framework to differential analysis. We also provide a user-friendly web based Graphic User Interface for biologists to use with microarray and RNAseq data. Bayesian inference can compensate for the instability of variance caused when using a small number of biological replicates by using pseudo replicates as prior knowledge. We also show that our new strategy to select pseudo replicates will improve the performance of the analysis. - See more at: http://www.eurekaselect.com/node/138761/article#sthash.VeK9xl5k.dpuf

Disaster management in Nepal: Media engagement in the Post-2015 Framework for Disaster Risk Reduction

Nepal, as a consequence of its geographical location and changing climate, faces frequent threats of natural disasters. According to the World Bank’s 2005 Natural Disasters Hotspots Report, Nepal is ranked the 11th most vulnerable country to earthquake and 30th to flood risk. Geo-Hazards International (2011) has classified Kathmandu as one of the world’s most vulnerable cities to earthquakes. In the last four decades more than 32,000 people in Nepal have lost their lives and annual monetary loss is estimated at more than 15 million (US) dollars. This review identifies gaps in knowledge, and progress towards implementation of the Post Hyogo Framework of Action. Nepal has identified priority areas: community resilience, sustainable development and climate change induced disaster risk reduction. However, one gap between policy and action lies in the ability of Nepal to act effectively in accordance with an appropriate framework for media activities. Supporting media agencies include the Press Council, Federation of Nepalese Journalists, Nepal Television, Radio Nepal and Telecommunications Authority and community based organizations. The challenge lies in further strengthening traditional and new media to undertake systematic work supported by government bodies and the National Risk Reduction Consortium (NRRC). Within this context, the ideal role for media is one that is proactive where journalists pay attention to a range of appropriate angles or frames when preparing and disseminating information. It is important to develop policy for effective information collection, sharing and dissemination in collaboration with Telecommunication, Media and Journalists. The aim of this paper is to describe the developments in disaster management in Nepal and their implications for media management. This study provides lessons for government, community and the media to help improve the framing of disaster messages. Significantly, the research highlights the prominence that should be given to flood, landslides, lightning and earthquakes.

Delegation of access in an Information Accountability Framework for eHealth

Shared eHealth records systems offer promising benefits for improving healthcare through high availability of information and improved decision making; however, their uptake has been hindered by concerns over the privacy of patient information. To address these privacy concerns while balancing the requirements of healthcare professionals to have access to the information they need to provide appropriate care, the use of an Information Accountability Framework (IAF) has been proposed. For the IAF and so called Accountable-eHealth systems to become a reality, the framework must provide for a diverse range of users and use cases. The initial IAF model did not provide for more diverse use cases including the need for certain users to delegate access to another user in the system to act on their behalf while maintaining accountability. In this paper, we define the requirements for delegation of access in the IAF, how such access policies would be represented in the Framework, and implement and validate an expanded IAF model.

Ecological infrastructure vs. techno-fix: A design framework for renewable energy infrastructure in public spaces

Renewable energy is commonly considered a technological addition to urban environments. By contrast, this PhD used a holistic approach to develop a design framework for integrating local electricity production into the ecological function and cultural use of public space. The framework addresses social engagement related to public interaction, and economic engagement related to the estimated quantity of electricity produced, in conjunction with environmental engagement related to the embodied energy required to construct the renewable energy infrastructure. The outcomes will contribute to social and environmental change by engaging society, enriching the local economy and increasing social networks.

Spontaneous and episodic volunteers: The legislative, insurance and risk management framework

Baby Boomers are a generation of life long association joiners, but following generations prefer spontaneous and episodic volunteering. This trend is apparent not only during natural disasters, but in most other spheres of volunteering. Legal liability for such volunteers is a growing concern, which unresolved, may dampen civic participation. We critically examine the current treatment of these liabilities through legislation, insurance and risk management.

A mathematical framework for expanding a railway’s theoretical capacity

Analytical techniques for measuring and planning railway capacity expansion activities have been considered in this article. A preliminary mathematical framework involving track duplication and section sub divisions is proposed for this task. In railways these features have a great effect on network performance and for this reason they have been considered. Additional motivations have also arisen from the limitations of prior models that have not included them.

A Bayesian Framework for the Assessment of Vision-based Weed and Fruit Detection and Classification Algorithms

This paper proposes new metrics and a performance-assessment framework for vision-based weed and fruit detection and classification algorithms. In order to compare algorithms, and make a decision on which one to use fora particular application, it is necessary to take into account that the performance obtained in a series of tests is subject to uncertainty. Such characterisation of uncertainty seems not to be captured by the performance metrics currently reported in the literature. Therefore, we pose the problem as a general problem of scientific inference, which arises out of incomplete information, and propose as a metric of performance the(posterior) predictive probabilities that the algorithms will provide a correct outcome for target and background detection. We detail the framework through which these predicted probabilities can be obtained, which is Bayesian in nature. As an illustration example, we apply the framework to the assessment of performance of four algorithms that could potentially be used in the detection of capsicums (peppers).

Designing innovative business models with a framework that promotes experimentation

Purpose – Business models to date have remained the creation of management, however, it is the belief of the authors that designers should be critically approaching, challenging and creating new business models as part of their practice. This belief portrays a new era where business model constructs become the new design brief of the future and fuel design and innovation to work together at the strategic level of an organisation. Design/methodology/approach – The purpose of this paper is to explore and investigate business model design. The research followed a deductive structured qualitative content analysis approach utilizing a predetermined categorization matrix. The analysis of forty business cases uncovered commonalities of key strategic drivers behind these innovative business models. Findings – Five business model typologies were derived from this content analysis, from which quick prototypes of new business models can be created. Research limitations/implications – Implications from this research suggest there is no “one right” model, but rather through experimentation, the generation of many unique and diverse concepts can result in greater possibilities for future innovation and sustained competitive advantage. Originality/value – This paper builds upon the emerging research and exploration into the importance and relevance of dynamic, design-driven approaches to the creation of innovative business models. These models aim to synthesize knowledge gained from real world examples into a tangible, accessible and provoking framework that provide new prototyping templates to aid the process of business model experimentation.

Creating a standardised teaching and learning framework for social work field placements

Mapping and evaluating a student's progress on placement is a core element of social work education but there has been scant attention to indicate how to effectively create and assess student learning and performance. This paper outlines a project undertaken by the Combined Schools of Social Work to develop a common learning and assessment tool that is being used by all social work schools in Victoria. The paper describes how the Common Assessment Tool (CAT) was developed, drawing on the Australian Association of Social Work Practice Standards, leading to seven key learning areas that form the basis of the assessment of a student's readiness for practice. An evaluation of the usefulness of the CAT was completed by field educators, liaison staff, and students, which confirmed that the CAT was a useful framework for evaluating students' learning goals. The feedback also identified a number of problematic features that were addressed in a revised CAT and rating scale.

The Learning Component of Dynamic Allocation Indices

For a multiarmed bandit problem with exponential discounting the optimal allocation rule is defined by a dynamic allocation index defined for each arm on its space. The index for an arm is equal to the expected immediate reward from the arm, with an upward adjustment reflecting any uncertainty about the prospects of obtaining rewards from the arm, and the possibilities of resolving those uncertainties by selecting that arm. Thus the learning component of the index is defined to be the difference between the index and the expected immediate reward. For two arms with the same expected immediate reward the learning component should be larger for the arm for which the reward rate is more uncertain. This is shown to be true for arms based on independent samples from a fixed distribution with an unknown parameter in the cases of Bernoulli and normal distributions, and similar results are obtained in other cases.