A hitchhiker's guide to assessing young people's physical activity : deciding what method to use

Researchers and practitioners interested in assessing physical activity in  children are often faced with the dilemma of what instrument to use. While there is a plethora of physical activity instruments to choose from, there is currently no guide regarding the suitability of common assessment instruments. The purpose of this paper is to provide a user’s guide for selecting physical activity assessment instruments appropriate for use with children and adolescents. While recommendations regarding specific instruments are not provided, the guide offers information about key attributes and considerations for the use of eight physical activity assessment approaches: heart rate monitoring; accelerometry; pedometry; direct observation; self-report; parent report; teacher report; and diaries/logs. Attributes of instruments and other factors to be considered in the selection of assessment instruments include: population (age); sample size; respondent burden; method/delivery mode; assessment time frame; physical activity information required (data output); data management; measurement error; cost (instrument and administration) and other limitations. A decision flow chart has been developed to assist researchers and practitioners to select an appropriate method of assessing physical activity. Five real-life scenarios are presented to illustrate this process in light of key instrument attributes. It is important that researchers, practitioners and policy makers understand the strengths and limitations of different methods of assessing physical activity, and are guided on selection of the most appropriate instrument/s to suit their needs.

Identification of concurrent control chart patterns with singular spectrum analysis and learning vector quantization

Identification of unnatural control chart patterns (CCPs) from manufacturing process measurements is a critical task in quality control as these patterns indicate that the manufacturing process is out-of-control. Recently, there have been numerous efforts in developing pattern recognition and classification methods based on artificial neural network to automatically recognize unnatural patterns. Most of them assume that a single type of unnatural pattern exists in process data. Due to this restrictive assumption, severe performance degradations are observed in these methods when unnatural concurrent CCPs present in process data. To address this problem, this paper proposes a novel approach based on singular spectrum analysis (SSA) and learning vector quantization network to identify concurrent CCPs. The main advantage of the proposed method is that it can be applied to the identification of concurrent CCPs in univariate manufacturing processes. Moreover, there are no permutation and scaling ambiguities in the CCPs recovered by the SSA. These desirable features make the proposed algorithm an attractive alternative for the identification of concurrent CCPs. Computer simulations and a real application for aluminium smelting processes confirm the superior performance of proposed algorithm for sets of typical concurrent CCPs.

Slope stability analysis for filled slopes using finite element limit analysis method

This paper uses the finite element upper and lower bound limit analysis to assess the stability of slopes mostly found in embankment cases where frictional materials are filled on purely cohesive undrained clay. For comparison purposes, the commonly used stability assessment method, limit equilibrium method (LEM) is also employed. The final results for both methods are then presented in the form of comprehensive chart solutions for the convenience of practicing engineers during preliminary slope designs. The failure mechanism will also be discussed in this paper. Ultimately, it should be noted that finite element limit analysis method holds the upper hand as its prior assumptions are not required. Thus, the obtained failure mechanism from the slope stability analysis will be more realistic. Hence, it will provide a better understanding for the slope failure surface. Therefore, engineers should design more carefully when the LEM is applied to the slopes with frictional materials filled on purely cohesive undrained clay. © 2014 American Society of Civil Engineers.

A transformation-based multivariate chart to monitor process dispersion

Multivariate monitoring techniques such as multivariate control charts are used to control the processes that contain more than one correlated characteristic. Although the majority of previous researches are focused on controlling only the mean vector of multivariate processes, little work has been performed to monitor the covariance matrix. In this research, a new method is presented to detect possible shifts in the covariance matrix of multivariate processes. The basis of the proposed method is to eliminate the correlation structure between the quality characteristics by transformation technique and then use an S chart for each variable. The performance of the proposed method is then compared to the ones from other existing methods and a real case is presented.