Interpreting behaviors from accelerometry: a method combining simplicity and objectivity.

Quantifying the behavior of motile, free-ranging animals is difficult. The accelerometry technique offers a method for recording behaviors but interpretation of the data is not straightforward. To date, analysis of such data has either involved subjective, study-specific assignments of behavior to acceleration data or the use of complex analyses based on machine learning. Here, we present a method for automatically classifying acceleration data to represent discrete, coarse-scale behaviors. The method centers on examining the shape of histograms of basic metrics readily derived from acceleration data to objectively determine threshold values by which to separate behaviors. Through application of this method to data collected on two distinct species with greatly differing behavioral repertoires, kittiwakes, and humans, the accuracy of this approach is demonstrated to be very high, comparable to that reported for other automated approaches already published. The method presented offers an alternative to existing methods as it uses biologically grounded arguments to distinguish behaviors, it is objective in determining values by which to separate these behaviors, and it is simple to implement, thus making it potentially widely applicable. The R script coding the method is provided.

Evidence for healthy cities : reflections on practice, method and theory

The European Healthy Cities project can be characterized as a social movement that employs an extremely wide range of political, social and behavioural interventions for the development and sustenance of urban population health. At all of these levels, the movement is inspired by ideological, theoretical and evidence-based perspectives. The result of this stance is a dynamic, complex and diverse landscape of initiatives, plans, programmes and actions. In quantitative terms (the number of WHO designated cities and associated cities and communities through national networks), ‘Healthy Cities’ can be regarded as an extraordinary accomplishment and a credit for both WHO and cities in the movement. In qualitative terms, however, critics of the movement have maintained that little evidence on its success and effectiveness has been generated. This critique finds its foundations in the mere perceptions of evidence, the politics of science and urban governance, and perspectives on the preferred or professed utilities of evidence-based health notions. The article reviews the nature of evidence and its interface with politics and governance. Applying a conceptual framework combining insights from knowledge utilization theory, theoretical perspectives on (health) policy development, theory-based evaluations and planned intervention approaches, it demonstrates that, although the evidence is overwhelming, there are barriers to the implementation of such evidence that should be further addressed by ‘Healthy Cities’.

A method for examining corporate social responsibility descriptions on SME websites

There is growing IS research concerning SME use of websites and limited but growing research on corporate social responsibility (CSR) by SMEs. However, to-date these two bodies of literature have remained largely separate. This paper links these fields by presenting an SME website content analysis method. Melville in his seminal MIS Quarterly article called for such methods which provide a nexus of IS, organisations and environment (which we extend to CSR). The method involves four steps: 1) identifying sources of SME websites; 2) determining if websites are describing CSR (based on the literature CSR by SMEs); 3) archiving website content for analysis; and 4) coding the website content using a structured framework (combining the literature on IS and CSR in an SME context). The paper also provides suggestions on how IS researchers can apply the method for quantitative and qualitative/exploratory objectives for future research.

Combining image regions and human activity for indirect object recognition in indoor wide-angle views

Traditional methods of object recognition are reliant on shape and so are very difficult to apply in cluttered, wideangle and low-detail views such as surveillance scenes. To address this, a method of indirect object recognition is proposed, where human activity is used to infer both the location and identity of objects. No shape analysis is necessary. The concept is dubbed 'interaction signatures', since the premise is that a human will interact with objects in ways characteristic of the function of that object - for example, a person sits in a chair and drinks from a cup. The human-centred approach means that recognition is possible in low-detail views and is largely invariant to the shape of objects within the same functional class. This paper implements a Bayesian network for classifying region patches with object labels, building upon our previous work in automatically segmenting and recognising a human's interactions with the objects. Experiments show that interaction signatures can successfully find and label objects in low-detail views and are equally effective at recognising test objects that differ markedly in appearance from the training objects.

Development and implementation of a novel measure for quantifying training loads in rowing : the T2minute method

Development and implementation of a novel measure for quantifying training loads in rowing: The T2minute method. J Strength Cond Res 28(4): 1172–1180, 2014—The systematic management of training requires accurate training load measurement. However, quantifying the training of elite Australian rowers is challenging because of (a) the multicenter, multistate structure of the national program; (b) the variety of training undertaken; and (c) the limitations of existing methods for quantifying the loads accumulated from varied training formats. Therefore, the purpose of this project was to develop a new measure for quantifying training loads in rowing (the T2minute method). Sport scientists and senior coaches at the National Rowing Center of Excellence collaborated to develop the measure, which incorporates training duration, intensity, and mode to quantify a single index of training load. To account for training at different intensities, the method uses standardized intensity zones (T zones) established at the Australian Institute of Sport. Each zone was assigned a weighting factor according to the curvilinear relationship between power output and blood lactate response. Each training mode was assigned a weighting factor based on whether coaches perceived it to be “harder” or “easier” than on-water rowing. A common measurement unit, the T2minute, was defined to normalize sessions in different modes to a single index of load; one T2minute is equivalent to 1 minute of on-water single scull rowing at T2 intensity (approximately 60–72% V[Combining Dot Above]O2max). The T2minute method was successfully implemented to support national training strategies in Australian high performance rowing. By incorporating duration, intensity, and mode, the T2minute method extends the concepts that underpin current load measures, providing 1 consistent system to quantify loads from varied training formats.

Identifying influential nodes in complex networks: A multiple attributes fusion method

How to identify influential nodes is still an open hot issue in complex networks. Lots of methods (e.g., degree centrality, betweenness centrality or K-shell) are based on the topology of a network. These methods work well in scale-free networks. In order to design a universal method suitable for networks with different topologies, this paper proposes a Multiple Attribute Fusion (MAF) method through combining topological attributes and diffused attributes of a node together. Two fusion strategies have been proposed in this paper. One is based on the attribute union (FU), and the other is based on the attribute ranking (FR). Simulation results in the Susceptible-Infected (SI) model show that our proposed method gains more information propagation efficiency in different types of networks. © 2014 Springer International Publishing.