Passive angle measurement based localization consistency via geometric constraints

In this paper, we examine the geometric relations between various measured parameters and their corresponding errors in angle-measurement based emitter localization scenarios. We derive a geometric constraint formulating the relationship among the measurement errors in such a scenario. Using this constraint, we formulate the localization task as a constrained optimization problem that can be performed on the measurements in order to provide the optimal values such that the solution is consistent with the underlying geometry.

α-linolenic acid and the risk of prostate cancer. What is the evidence?

Purpose
Several studies have examined the association between polyunsaturated fatty acids and prostate cancer risk. We evaluated the evidence on the association between the essential polyunsaturated fatty acid, known as α-linolenic acid, and the risk of prostate cancer in humans.
Materials and Methods
We comprehensively reviewed published studies on the association between α-linolenic acid and the risk of prostate cancer using MEDLINE.
Results
A number of studies have shown a positive association between dietary, plasma or red blood cell levels of α-linolenic acid and prostate cancer. Other studies have demonstrated either no association or a negative association. The limitations of these studies include the assumption that dietary or plasma α-linolenic acid levels are positively associated with prostate tissue α-linolenic acid levels, and measurement errors of dietary, plasma and red blood cell α-linolenic acid levels.
Conclusions
More research is needed in this area before it can be concluded that there is an association between α-linolenic acid and prostate cancer.

Bayesian classification of catchments using spatial data: a first step to improved modelling of catchment effects on stream ecological condition

A major challenge facing freshwater ecologists and managers is the development of models that link stream ecological condition to catchment scale effects, such as land use. Previous attempts to make such models have followed two general approaches. The bottom-up approach employs mechanistic models, which can quickly become too complex to be useful. The top-down approach employs empirical models derived from large data sets, and has often suffered from large amounts of unexplained variation in stream condition.

We believe that the lack of success of both modelling approaches may be at least partly explained by scientists considering too wide a breadth of catchment type. Thus, we believe that by stratifying large sets of catchments into groups of similar types prior to modelling, both types of models may be improved. This paper describes preliminary work using a Bayesian classification software package, ‘Autoclass’ (Cheeseman and Stutz 1996) to create classes of catchments within the Murray Darling Basin based on physiographic data.

Autoclass uses a model-based classification method that employs finite mixture modelling and trades off model fit versus complexity, leading to a parsimonious solution. The software provides information on the posterior probability that the classification is ‘correct’ and also probabilities for alternative classifications. The importance of each attribute in defining the individual classes is calculated and presented, assisting description of the classes. Each case is ‘assigned’ to a class based on membership probability, but the probability of membership of other classes is also provided. This feature deals very well with cases that do not fit neatly into a larger class. Lastly, Autoclass requires the user to specify the measurement error of continuous variables.

Catchments were derived from the Australian digital elevation model. Physiographic data werederived from national spatial data sets. There was very little information on measurement errors for the spatial data, and so a conservative error of 5% of data range was adopted for all continuous attributes. The incorporation of uncertainty into spatial data sets remains a research challenge.

The results of the classification were very encouraging. The software found nine classes of catchments in the Murray Darling Basin. The classes grouped together geographically, and followed altitude and latitude gradients, despite the fact that these variables were not included in the classification. Descriptions of the classes reveal very different physiographic environments, ranging from dry and flat catchments (i.e. lowlands), through to wet and hilly catchments (i.e. mountainous areas). Rainfall and slope were two important discriminators between classes. These two attributes, in particular, will affect the ways in which the stream interacts with the catchment, and can thus be expected to modify the effects of land use change on ecological condition. Thus, realistic models of the effects of land use change on streams would differ between the different types of catchments, and sound management practices will differ.

A small number of catchments were assigned to their primary class with relatively low probability. These catchments lie on the boundaries of groups of catchments, with the second most likely class being an adjacent group. The locations of these ‘uncertain’ catchments show that the Bayesian classification dealt well with cases that do not fit neatly into larger classes.

Although the results are intuitive, we cannot yet assess whether the classifications described in this paper would assist the modelling of catchment scale effects on stream ecological condition. It is most likely that catchment classification and modelling will be an iterative process, where the needs of the model are used to guide classification, and the results of classifications used to suggest further refinements to models.

Exploiting geometry for improved hybrid AOA/TDOA-based localization

In this paper we examine the geometrically constrained optimization approach to localization with hybrid bearing (angle of arrival, AOA) and time difference of  arrival (TDOA) sensors. In particular, we formulate a constraint on the measurement errors which is then used along with constraint-based optimization tools in order to estimate the maximum likelihood values of the errors given an appropriate cost function. In particular we focus on deriving a localization algorithm for stationary target localization in the so-called adverse localization geometries where the relative positioning of the sensors and the target do not readily permit accurate or convergent localization using traditional approaches. We illustrate this point via simulation and we compare our approach to a number of different techniques that are discussed in the literature.

Robust performance testing for digital forensic tools

In previous work, the authors presented a theoretical lower bound on the required number of testing runs for performance testing of digital forensic tools. However, experimental errors are inevitable in laboratory settings, occurring as measurement errors or as random errors and can result in practical situations where the number of testing runs is far from the theoretical bound. This paper adapts our former work to tolerate such errors in the testing results. The contribution of our new methodology enables the tester to achieve performance testing results of high quality from a manageable number of observations and in a dynamic but controllable way. This is of particular interest to forensic testers who do not have access to sophisticated equipment and who can allocate only a small amount of time to testing.

Bearing-only localization using geometrically constrained optimization

We examine the problem of optimal bearing-only localization of a single target using synchronous measurements from multiple sensors. We approach the problem by forming geometric relationships between the measured parameters and their corresponding errors in the relevant emitter localization scenarios. Specifically, we derive a geometric constraint equation on the measurement errors in such a scenario. Using this constraint, we formulate the localization task as a constrained optimization problem that can be performed on the measurements in order to provide the optimal values such that the solution is consistent with the underlying geometry. We illustrate and confirm the advantages of our approach through simulation, offering detailed comparison with traditional maximum likelihood (TML) estimation.

Whale watching disrupts feeding activities of minke whales on a feeding ground

Human disturbances of wildlife, such as tourism, can alter the activities of targeted individuals. Repeated behavioural disruptions can have long-term consequences for individual vital rates (survival and reproduction). To manage these sub-lethal impacts, we need to understand how activity disruptions can influence bioenergetics and ultimately individual vital rates. Empirical studies of the mechanistic links between whale-watching boat exposure and behavioural variation and vital rates are currently lacking for baleen whales (mysticetes). We compared minke whale Balaenoptera acutorostrata behaviour on a feeding ground in the presence and absence of whale-watching boats. Effects on activity states were inferred from changes in movement metric data as well as the occurrence of surface feeding events. Linear mixed effects models and generalised estimation equations were used to investigate the effect of whale-watching boat interactions. Measurement errors were quantified, and their effects on model parameter estimates were investigated using resampling methods. Minke whales responded to whale-watching boats by performing shorter dives and increased sinuous movement. A reduction in the probability of observing longer inter-breath intervals during sinuous movement showed that whale-watching boat interactions reduced foraging activity. Further, the probability of observing surface feeding events also decreased during interactions with whale-watching boats. This indicates that whalewatching boats disrupted the feeding activities of minke whales. Since minke whales are capital breeders, a decrease in feeding success on the feeding grounds due to whale-watching boats could lead to a decrease in energy available for foetus development and nursing on the breeding grounds. Such impact could therefore alter the calving success of this species.

Validity of an upper-body-mounted accelerometer to measure peak vertical and resultant force during running and change-of-direction tasks

This study assessed the validity of a tri-axial accelerometer worn on the upper body to estimate peak forces during running and change-of-direction tasks. Seventeen participants completed four different running and change-of-direction tasks (0°, 45°, 90°, and 180°; five trials per condition). Peak crania-caudal and resultant acceleration was converted to force and compared against peak force plate ground reaction force (GRF) in two formats (raw and smoothed). The resultant smoothed (10 Hz) and crania-caudal raw (except 180°) accelerometer values were not significantly different to resultant and vertical GRF for all running and change-of-direction tasks, respectively. Resultant accelerometer measures showed no to strong significant correlations (r = 0.00–0.76) and moderate to large measurement errors (coefficient of variation [CV] = 11.7–23.9%). Crania-caudal accelerometer measures showed small to moderate correlations (r = − 0.26 to 0.39) and moderate to large measurement errors (CV = 15.0–20.6%). Accelerometers, within integrated micro-technology tracking devices and worn on the upper body, can provide a relative measure of peak impact force experienced during running and two change-of-direction tasks (45° and 90°) provided that resultant smoothed values are used.

Measuring physical activity in a cardiac rehabilitation population using a smartphone-based questionnaire

BACKGROUND: Questionnaires are commonly used to assess physical activity in large population-based studies because of their low cost and convenience. Many self-report physical activity questionnaires have been shown to be valid and reliable measures, but they are subject to measurement errors and misreporting, often due to lengthy recall periods. Mobile phones offer a novel approach to measure self-reported physical activity on a daily basis and offer real-time data collection with the potential to enhance recall.

OBJECTIVE: The aims of this study were to determine the convergent validity of a mobile phone physical activity (MobilePAL) questionnaire against accelerometry in people with cardiovascular disease (CVD), and to compare how the MobilePAL questionnaire performed compared with the commonly used self-recall International Physical Activity Questionnaire (IPAQ).

METHODS: Thirty adults aged 49 to 85 years with CVD were recruited from a local exercise-based cardiac rehabilitation clinic in Auckland, New Zealand. All participants completed a demographics questionnaire and underwent a 6-minute walk test at the first visit. Subsequently, participants were temporarily provided a smartphone (with the MobilePAL questionnaire preloaded that asked 2 questions daily) and an accelerometer, which was to be worn for 7 days. After 1 week, a follow-up visit was completed during which the smartphone and accelerometer were returned, and participants completed the IPAQ.

RESULTS: Average daily physical activity level measured using the MobilePAL questionnaire showed moderate correlation (r=.45; P=.01) with daily activity counts per minute (Acc_CPM) and estimated metabolic equivalents (MET) (r=.45; P=.01) measured using the accelerometer. Both MobilePAL (beta=.42; P=.008) and age (beta=-.48, P=.002) were significantly associated with Acc_CPM (adjusted R(2)=.40). When IPAQ-derived energy expenditure, measured in MET-minutes per week (IPAQ_met), was considered in the predicted model, both IPAQ_met (beta=.51; P=.001) and age (beta=-.36; P=.016) made unique contributions (adjusted R(2)=.47, F2,27=13.58; P<.001).There was also a significant association between the MobilePAL and IPAQ measures (r=.49, beta=.51; P=.007).

CONCLUSIONS: A mobile phone-delivered questionnaire is a relatively reliable and valid measure of physical activity in a CVD cohort. Reliability and validity measures in the present study are comparable to existing self-report measures. Given their ubiquitous use, mobile phones may be an effective method for physical activity surveillance data collection.

Integrating errors into developmental assessment: 'time' for ages 8-13

It is widely agreed that measurement is of paramount importance to students’ overall development in mathematics. This paper describes a developmental ‘map’ of students’ understanding and skills in measurement, focussed on the topic of Time, that integrates correct and incorrect student ideas. The map is based on a Rasch analysis of data from a large-scale UK national survey for standardising assessment for children from 5 to 14 years of age. It is demonstrated how a partial credit strategy enables a developmental map to be constructed to show students’ strengths and weaknesses in a meaningful and useful summative and formative manner. This map provides evidence, of both a summative and a formative nature, which may enable teachers to craft appropriate and successful learning experiences for children.

Measurement of the refractive index of soft contact lenses during wear

Purpose: To determine whether the refractive index (RI) of a soft contact lens can be evaluated using refractometry while the lens remains on the eye and to compare this with more traditional ex vivo RI measurements.

Methods: A slitlamp apparatus was modified to incorporate a customized Atago hand refractometer. With a double-masked study design, nine adapted symptomatic soft contact lens wearers wore a contact lens in each eye (lotrafilcon B and etafilcon A) in a randomized order. In vivo RI was determined from the relative Brix scale measurements immediately after lens insertion and after 1 and 10 hr of lens wear. Ex vivo refractometry was performed after 10 hr of lens wear for comparison. Means ± standard errors of the means are reported.

Results: In vivo RI values at baseline were 1.422 ± 0.0004 (lotrafilcon B) and 1.405 ± 0.0021 (etafilcon A); after 1 hr of lens wear, values were 1.423 ± 0.0006 and 1.408 ± 0.0007, respectively; and after 10 hr of lens wear, values were 1.424 ± 0.0004 and 1.411 ± 0.0010, respectively. Ex vivo RI values at the end of the 10 hr wearing period were 1.424 ± 0.0003 (lotrafilcon B) and 1.412 ± 0.0017 (etafilcon A). The change in in vivo RI across the day was statistically significant for the etafilcon A lens (repeated-measures analysis of variance, P<0.01) but not for the lotrafilcon B lens (P>0.05).

Conclusions: This novel adaptation of refractometry was able to measure the RI of soft contact lenses during wear (without lens removal). End of day RI measurements using in vivo and ex vivo refractometry were comparable with each other. Future work is required to determine whether this in vivo method can improve our understanding of the relationships between soft contact lens RI, hydration, on-eye lens performance, and symptomology.

Measurement error causes scale-dependent threshold erosion of biological signals in animal movement data

Recent advances in telemetry technology have created a wealth of tracking data available for many animal species moving over spatial scales from tens of meters to tens of thousands of kilometers. Increasingly, such data sets are being used for quantitative movement analyses aimed at extracting fundamental biological signals such as optimal searching behavior and scale-dependent foraging decisions. We show here that the location error inherent in various tracking technologies reduces the ability to detect patterns of behavior within movements. Our analyses endeavored to set out a series of initial ground rules for ecologists to help ensure that sampling noise is not misinterpreted as a real biological signal. We simulated animal movement tracks using specialized random walks known as Lévy flights at three spatial scales of investigation: 100-km, 10-km, and 1-km maximum daily step lengths. The locations generated in the simulations were then blurred using known error distributions associated with commonly applied tracking methods: the Global Positioning System (GPS), Argos polar-orbiting satellites, and light-level geolocation. Deviations from the idealized Lévy flight pattern were assessed for each track after incrementing levels of location error were applied at each spatial scale, with additional assessments of the effect of error on scale-dependent movement patterns measured using fractal mean dimension and first-passage time (FPT) analyses. The accuracy of parameter estimation (Lévy μ, fractal mean D, and variance in FPT) declined precipitously at threshold errors relative to each spatial scale. At 100-km maximum daily step lengths, error standard deviations of ≥10 km seriously eroded the biological patterns evident in the simulated tracks, with analogous thresholds at the 10-km and 1-km scales (error SD ≥ 1.3 km and 0.07 km, respectively). Temporal subsampling of the simulated tracks maintained some elements of the biological signals depending on error level and spatial scale. Failure to account for large errors relative to the scale of movement can produce substantial biases in the interpretation of movement patterns. This study provides researchers with a framework for understanding the limitations of their data and identifies how temporal subsampling can help to reduce the influence of spatial error on their conclusions.