Separate Metrics Research Articles

Low-Fidelity Hybrid Sexual Assault Simulation Training's Effect on the Comfort and Competency of Resident Physicians

BackgroundAlternative training methods are needed for resident physicians to ensure that care is not compromised should they practice in settings without well-established Sexual Assault Nurse Examiner (SANE) programs. ObjectiveThe purpose of this study is to determine the effectiveness of a simulation-based sexual assault response course for resident physicians at an institution without an on-site SANE program. MethodsEducational intervention study of 12 emergency medicine residents using a low-fidelity hybrid simulation model. The study was comprised of eight male and four female physicians at a military medical center in San Diego, CA. Assessment occurred using three separate metrics. The first was a written knowledge test. The second was a simulated interview and evidentiary examination. These metrics were given 1 month before and 3 months after an 8-h training course. The final metric was Likert-scale questionnaires surveying pre- and post-course feelings of competency and comfort. ResultsThe emergency medicine residents showed a 13% improvement (95% confidence interval [CI] 7−20%) in written examination scores pre and post intervention. Post-course interview and examinations reflected a 44% improvement (95% CI 24−64%) in critical action completion. Pre-course comfort and competency questionnaires were a median of 2 (interquartile range [IQR] 1−3) on a Likert Scale. Post-course survey responses were a median of 4 (IQR 2−5). ConclusionsLow-fidelity hybrid simulation is a useful tool to train inexperienced physicians to perform evidentiary examinations and interviews without sacrificing the privacy and direct care of sexual assault victims.

The association between obesity and outcomes in critically ill patients.

Obesity rates are increasing worldwide, particularly in North America. The impact of obesity on the outcome of critically ill patients is unclear. A prospective observational cohort study of consecutive patients admitted to a tertiary critical care unit in Canada between January 10, 2008 and March 31, 2009 was conducted. Exclusion criteria were age <18 years, admission <24 h, planned cardiac surgery, pregnancy, significant ascites, unclosed surgical abdomen and brain death on admission. Height, weight and abdominal circumference were measured at the time of intensive care unit (ICU) admission. Coprimary end points were ICU mortality and a composite of ICU mortality, reintubation, ventilator-associated pneumonia, line sepsis and ICU readmission. Subjects were stratified as obese or nonobese, using two separate metrics: body mass index (BMI) ≥ 30 kg/m(2) and a novel measurement of 75th percentile for waist-to-height ratio (WHR). Among 449 subjects with a BMI ≥ 18.5 kg/m(2), both BMI and WHR were available for comparative analysis in 348 (77.5%). Neither measure of obesity was associated with the primary end points. BMI ≥ 3 0 kg/m(2) was associated with a lower odds of six-month mortality than the BMI <30 kg/m(2) group (adjusted OR 0.59 [95% CI 0.36 to 0.97]; P=0.04) but longer intubation times (adjusted RR 1.56 [95% CI 1.17 to 2.07]; P=0.003) and longer ICU length of stay (adjusted RR 1.67 [95% CI 1.21 to 2.31]; P=0.002). Conversely, measurement of 75th percentile for WHR was associated only with decreased ICU readmission (OR 0.23 [95% CI 0.07 to 0.79]; P=0.02). Obesity was not necessarily associated with worse outcomes in critically ill patients.

Commute mode and mental health in major metropolitan areas

This research studies the relationship between travel behavior and mental health by exploring the association between commute mode and different mental health issues. Data were drawn from the 68 substate regions that have the most workers per square mile within the United States of America. Commute mode data were collected from the American Community Survey (ACS), while four separate metrics of mental health were collected from the National Survey on Drug Use and Health (NSDUH). After analyzing through a Spearman’s rank-order correlation and a linear regression, both carpooling and public transportation were found to have a strong and statistically significant negative association with the mental health issues, while driving alone to work was found to have a strong and statistically significant positive association. Bicycling and walking were not found to have a significant association.

Classification of Hamilton-Jacobi separation in orthogonal coordinates with diagonal curvature

We find all orthogonal metrics where the geodesic Hamilton-Jacobi equation separates and the Riemann curvature tensor satisfies a certain equation (called the diagonal curvature condition). All orthogonal metrics of constant curvature satisfy the diagonal curvature condition. The metrics we find either correspond to a Benenti system or are warped product metrics where the induced metric on the base manifold corresponds to a Benenti system. Furthermore, we show that most metrics we find are characterized by concircular tensors; these metrics, called Kalnins-Eisenhart-Miller metrics, have an intrinsic characterization which can be used to obtain them on a given space. In conjunction with other results, we show that the metrics we found constitute all separable metrics for Riemannian spaces of constant curvature and de Sitter space.

A characterization of the effect of limb position on EMG features to guide the development of effective prosthetic control schemes.

Electromyogram (EMG) pattern recognition has long been used for the control of upper limb prostheses. More recently, it has been shown that variability induced during functional use, such as changes in limb position and dynamic contractions, can have a substantial impact on the robustness of EMG pattern recognition. This work further investigates the reasons for pattern recognition performance degradation due to the limb position variation. The main focus is on the impact of limb position variation on features of the EMG, as measured using separability and repeatability metrics. The results show that when the limb is moved to a position different from the one in which the classifier is trained, both the separability and repeatability of the data decrease. It is shown how two previously proposed classification methods, multiple position training and dual-stage classification, resolve the position effect problem to some extent through increasing either separability or repeatability but not both. A hybrid classification method which exhibits a compromise between separability and repeatability is proposed in this work. It is shown that, when tested with the limb in 16 different positions, this method increases classification accuracy from an average of 70% (single position training) to 89% (hybrid approach). This hybrid method significantly (p<;0.05) outperforms multiple position training (an average of 86%) and dual-stage classification (an average of 85%).

Real-time recognition of sows in video: A supervised approach

This paper proposes a supervised classification approach for the real-time pattern recognition of sows in an animal supervision system (asup). Our approach offers the possibility of the foreground subtraction in an asup’s image processing module where there is lack of statistical information regarding the background. A set of 7 farrowing sessions of sows, during day and night, have been captured (approximately 7days/sow), which is used for this study. The frames of these recordings have been grabbed with a time shift of 20s. A collection of 215 frames of 7 different sows with the same lighting condition have been marked and used as the training set. Based on small neighborhoods around a point, a number of image local features are defined, and their separability and performance metrics are compared. For the classification task, a feed-forward neural network (NN) is studied and a realistic configuration in terms of an acceptable level of accuracy and computation time is chosen. The results show that the dense neighborhood feature (d.3×3) is the smallest local set of features with an acceptable level of separability, while it has no negative effect on the complexity of NN. The results also confirm that a significant amount of the desired pattern is accurately detected, even in situations where a portion of the body of a sow is covered by the crate’s elements. The performance of the proposed feature set coupled with our chosen configuration reached the rate of 8.5fps. The true positive rate (TPR) of the classifier is 84.6%, while the false negative rate (FNR) is only about 3%. A comparison between linear logistic regression and NN shows the highly non-linear nature of our proposed set of features.

Evaluating the effects of liming and wood-ash treatment on forest ecosystems through systematic meta-analysis

Liming and wood-ash addition have long been used to attenuate the effects of acidic deposition on forest soils with the goal of promoting tree growth. We performed quantitative meta-analyses of treatment studies from managed forest ecosystems to assess general tendencies of effects of treatment on seven selected measures of performance thought to reasonably reflect the effects of Ca-addition treatment. We retrieved over 350 independent trials from 110 peer-reviewed liming and wood-ash addition studies that were integrated to determine soil pH, base saturation (BS), tree foliar Ca concentration, tree growth, ectomychorrhizae root colonization, soil C-to-N ratio, and microbial indices. The results were quantified through three separate meta-analysis effect size metrics: unweighted relative values and two weighted metrics, Hedges’ d and ln R. A surprising number of treatment trials (22%–85%) reported no significant effect, and soil pH and foliar Ca appeared more responsive to liming than to wood-ash addition, whereas BS and tree growth appeared more responsive to wood-ash addition. For six of the seven parameters, estimated mean effect sizes were similar in magnitude and positive in direction for all three meta-analysis metrics. Regression tree optimal models explained 38% of the variation in pH, 47% of the variation in BS, 51% of the variation in foliar Ca concentration, and 26% of the variation in tree growth. The largest predictors of effect size, within our selected group, were as follows: soil type for pH; soil type, trial duration in years, and species (hardwood or softwood) for BS; treatment dose and type for foliar Ca concentration; and trial duration, initial soil pH, and tree species for tree growth. This analysis shows that Ca additions are not universally beneficial and provides insight into when Ca additions to forest soils are likely to be most effective.

What is the relationship between online activity and driving-licence-holding amongst young adults?

There is growing interest amongst both practitioners and researchers in the correlates of young adults’ driving-licence-acquisition. One aspect of the ongoing scholarly debate is whether taking part in online (i.e. virtual) activities may be associated with young adults feeling less need to drive and hence to acquire a driving licence. This paper addresses this issue by drawing on analysis of two distinct datasets. Both contain rich pseudo-diary instruments in which people indicate detailed characteristics of their unique online-activity profile. This includes both indicators of the types of online activities in which respondents participate, and a separate metric of internet-use intensity. The latter is defined in one dataset as the amount of time/week spent online, and in the other dataset the frequency of their internet use. On the basis of a set of multivariate regression analyses, a positive (i.e. complementary) cross-sectional relationship between young adults’ online activity and licence-holding was found. We find that young adults who use the internet are, net of confounding effects, more likely to hold a driving licence than otherwise identical young adults who do not use the internet. Both datasets show this type of effect, and it is robust across a range of model specifications, including multi-stage estimations to address cross-correlation between indicators of internet usage. In addition to the positive net statistical association, we also report several other noteworthy effects. Of the six effects associated with online-activity types that are directly comparable between the two datasets, we find that the correlation in the parameter estimates across the two datasets is 0.63. This suggests similar types of relationships across the two datasets. Also, in several (but not all) of our analyses we found an inverted ‘U’ shaped ceteris paribus relationship between intensity-of-internet-use and licence-holding. The positive net statistical association between internet use and licence-holding is a different relationship than previously reported in the literature, and therefore further research is needed to reconcile the differences (which are likely due at least in part to different methodological approaches and data resources). Further research is also needed to continue to resolve between the relative saliency of other hypothesised determinants of licence-holding (e.g. economic and socio-demographic explanators, as well as licence-acquisition regimes that vary by time and place).

Time Series Analysis of Land Cover Change: Developing Statistical Tools to Determine Significance of Land Cover Changes in Persistence Analyses

Despite the existence of long term remotely sensed datasets, change detection methods are limited and often remain an obstacle to the effective use of time series approaches in remote sensing applications to Land Change Science. This paper establishes some simple statistical tests to be applied to NDVI-derived time series of remotely sensed data products. Specifically, the methods determine the statistical significance of three separate metrics of the persistence of vegetation cover or changes within a landscape by comparison to various forms of “benchmarks”; directional persistence (changes in sign relative to some fixed reference value), relative directional persistence (changes in sign relative to the preceding value), and massive persistence (changes in magnitude relative to the preceding value). Null hypotheses are developed on the basis of serially independent, normally distributed random variables. Critical values are established theoretically through consideration of the numeric properties of those variables, application of extensive Monte Carlo simulations, and parallels to random walk processes. Monthly pixel-level NDVI values for the state of Florida are analyzed over 25 years, illustrating the techniques’ abilities to identify areas and/or times of significant change, and facilitate a more detailed understanding of this landscape. The potential power and utility of such techniques is diverse within the area of remote sensing studies and Land Change Science, especially in the context of global change.

Objective measures of blind source separation and the intelligibility of separated speech

When multiple acoustic sources are present in an environment monitored by multiple microphones, each microphone’s response signal is typically a weighted and delayed mixture of the active source signals. Subject to important constraints, these mixtures can be processed using Blind Source Separation (BSS) methods to construct output signals representative of each of the “hidden” acoustic sources in isolation. An important objective measure of the quality of a given “separation solution” is the amount of mutual information remaining in the BSS output channels: the less mutual information across channels, the better the separation. Often, the acoustic signals of interest in a complex acoustic environment are speech sources, and the measure of separation quality that really matters to listeners is speech intelligibility—a subjective (perceptual) measure that cannot be directly computed by a signal processing system. Thus, it would be useful to know how well objective measures of separation quality, such as mutual information, correlate with intelligibility. We present results of perceptual and objective tests exploring the relationship between objective separation metrics and word intelligibility as estimated by listeners’ responses in word identification tasks, for target-word-in-carrier-phrase utterances recorded in an acoustic environment with masking speech babble and broadband noise sources.

Extinctions and the loss of ecological function in island bird communities

AbstractAimBecause of the negative impact that ongoing biodiversity loss may have on ecosystem properties that are critical for humans, understanding the relationship between extinction and functional diversity over time is of critical importance for conservation. However, empirical evidence concerning the sensitivity of vertebrate community function to species loss is very limited. Here we assess documented prehistoric and historic extinctions of birds on Pacific islands in an effort to quantify the consequences of extinctions for functional structure and diversity in natural communities over broad spatial scales.LocationForty‐four islands from across the Pacific.MethodsWe estimated functional aspects of island bird communities before and after Holocene extinctions based on body size, foraging niche, diet and activity period. We used four separate metrics to measure ecological function: functional diversity (FD), functional richness (FRic), functional evenness and functional divergence. We employed null models to separate the effects of observed extinctions from changes expected due to declining species richness.ResultsWe find that Holocene bird extinctions led to substantial changes in community‐level functional diversity. Observed declines in FD and FRic were predictable from the pre‐extinction composition of communities, and did not differ from null model expectations. Across all islands, we observed non‐random changes in functional trait composition, with shifts away from ground‐level foraging, granivory and herbivory after extinctions. Extinctions have resulted in the loss of up to 80% of original functional diversity on some islands and caused a sharp decrease in the variety of ecological functions provided by birds.Main conclusionsOur findings illustrate the significant losses of functional diversity that are already taking place on many islands and demonstrate its close connection with the loss of species. Accounting for the functional roles of species allows a more integrative understanding of ecological function and helps to bridge species and ecosystem perspectives in conservation science.

微纳流体样品片上分离技术

Microfluidic separation of cells and particles, which is an essential preparatory step and has recently gained significant attention in sample preparations for biological and chemical assays, is currently developing rapidly where key areas of interest are found in designing lab-on-a-chip systems that offer controlled microenvironments for studies of fundamental cell biology. This review outlines the current state-of-the-art in microfluidic technology for cell or particle separation in continuous flow based systems. Microfluidics-based sorting offers numerous advantages, including reducing sample volumes, faster sample processing, high sensitivity and spatial resolution, low cost, and increased portability. A large variety of separation principles, which are classified into active and passive separation methods depending on the operating principles, are discussed in details along with popular examples demonstrating their application toward cell separation. Common separation metrics, including separation markers, resolution, efficiency, and throughput of these techniques are discussed. Meanwhile, the drawbacks of the different separation theories are also described. High efficient microscale separation methods are likely to play an important role in realizing true point-of-care(POC), lab-on-a-chip(LOC) and in-the-field analysis devices. Therefore, this field has caused the researchers' interest which could promote the separation technology and testing, signal readout to integrate together. This will help to develop a portable diagnosis and treat device in the future.

Smart low flow signature metrics for an improved overall performance evaluation of hydrological models

Hydrological models have to be calibrated accurately to provide reasonable model results. For a concise model evaluation, the different phases of the hydrograph have to be considered in multi-metric frameworks with appropriate performance metrics. Low and high flows need to be reproduced simultaneously without neglecting the other phases of the hydrograph. In this paper, we highlight the relevance of very low and low flows with separate performance metrics. We present a multi-metric evaluation framework to identify calibration runs, which represent the different phases of the hydrograph precisely. A stepwise evaluation was done with commonly used statistical performance metrics (Nash–Sutcliffe, percent bias) and signature metrics, which are based on the flow duration curve (FDC). In order to consider a fairly balanced evaluation between high and low flow phases, we divided the flow duration curve into segments of high, medium and low flow phases, and additionally into very high and very low flow phases. The model performance in these segments was evaluated separately with the root mean square error (RMSE). Our results show that this evaluation method leads to an improved selection of good calibration runs to enhance the overall model performance by the refined segmentation of FDC. By combining performance metrics for high flow conditions with low flow conditions, this study demonstrates the challenge of calibrating a model with a satisfactory performance in high and low phases simultaneously. Consequently, we conclude that an additional performance metric for very low flows should be included in model analyzes to improve the overall performance in all phases of the hydrograph.

The use of GNSS technology to identify lambing behaviour in pregnant grazing Merino ewes

This current study investigated whether pre-lambing behavioural changes could be identified with the use of global navigation satellite system (GNSS) technology. GNSS devices were deployed on 20 pregnant Merino ewes grazing a 1.6 ha paddock and their lambing activity was compared with the metrics derived from the spatial data. The aims were to evaluate the lambing event using the following three separate metrics: (1) mean daily speed (MDS) of ewes 7 days before and to 7 days after lambing, inclusive (n = 12); (2) mean hourly speed (MHS) 12 h before and 12 h after lambing, inclusive (n = 9); and (3) the mean distance the lambing ewe to her peers in the 7 days before and the 7 days after lambing (mean distance to peers (MDP); n = 9), inclusive. There was a significant (P &lt; 0.01) difference between pre- and post-lambing MDS with average ± se MDS pre-lambing being faster than post-lambing (0.051 ± 0.0004 vs 0.047 ± 0.0005 m/s). Pre- and post-lambing MHS differed significantly (P &lt; 0.05), with mean ± s.e. MHS pre-lambing being faster than post-lambing (0.049 ± 0.002 vs 0.038 ± 0.002 m/s). Mean distance to peers indicated that at the time of lambing, ewes were significantly (P &lt; 0.01) further from their peers than at either pre- or post-lambing (83.6 ± 14.59 vs 35.2 ± 2.82 vs 35.6 ± 1.68 m). Despite MDS and MHS metrics indicating significant changes pre- and post-lambing, neither metric was able to identify the time of lambing. The MDP metric could not identify differences pre- and post-lambing but was useful at predicting lambing. The current study found that MDS and MHS metrics have the potential to determine a ‘trigger’ point that could identify parturition and therefore could be used to determine the day of lambing. Therefore, further research is required to determine if a combination of these metrics could identify pre-lambing activity that would enable informed management decisions to be made.

Quantification of left ventricular deformation fields from undersampled radial, real-time cardiac MRI

Background Regional ventricular elasticity is altered in ischemic heart disease and LV hypertrophy, potentially contributing to and sustaining a ventricular remodeling process. Conventional methods to measure regional ventricular elasticity in preclinical animal models require high fidelity ventricular pressure measurement, sonomicrometry transducers and dynamic modulation of preload. These procedures are highly invasive and provide only limited regional information; patient studies are prohibitive. We dynamically measure global LV elastance using undersampled radial real-time MRI and seek to find regional LV elastance in these studies. This requires tracking LV motion derived from wall thickness measurements in a large number of real-time image frames at high accuracy. We assessed several motion tracking algorithms and characterize their fidelity with respect to true motion labeled by experts and sensitivity to residual radial streak artifact. Methods A series of short axis MR slices were acquired in 9 patients using a golden-angle radial bSSFP trajectory (total views = 4000, radial views per image = 34, shared profiles = 31, reconstructed frame rate = 98 fps) and reconstructed via conjugate gradient-SENSE based iterative reconstruction. Two independent researchers manually segmented the datasets and the movies were restricted to three full heartbeats to limit the number of images. Image registrations were performed between the initial end-diastolic frame and all subsequent frames. The image registrations were performed using b-spline and symmetric diffeomorphic (SyN) image registration across a range of spatial scales (shown in Table 1). Four separate image registration metrics were investigated (mutual information, meansquared overlap, cross-correlation, and demons). Although we expected mean squares and cross-correlation to perform better in this intramodality registration, radial streaking could potentially corrupt data integrity. The resulting deformation fields were evaluated using an array of metrics (dice coefficient, area overlap, false positive, and false negative rate) for all registrations, image frames and patients.

The Use of Ammonium Formate as a Mobile-Phase Modifier for LC-MS/MS Analysis of Tryptic Digests

A major challenge facing current mass spectrometry (MS)-based proteomics research is the large concentration range displayed in biological systems, which far exceeds the dynamic range of commonly available mass spectrometers. One approach to overcome this limitation is to improve online reversed-phase liquid chromatography (RP-LC) separation methodologies. LC mobile-phase modifiers are used to improve peak shape and increase sample load tolerance. Trifluoroacetic acid (TFA) is a commonly used mobile-phase modifier, as it produces peptide separations that are far superior to other additives. However, TFA leads to signal suppression when incorporated with electrospray ionization (ESI), and thus, other modifiers, such as formic acid (FA), are used for LC-MS applications. FA exhibits significantly less signal suppression, but is not as effective of a modifier as TFA. An alternative mobile-phase modifier is the combination of FA and ammonium formate (AF), which has been shown to improve peptide separations. The ESI-MS compatibility of this modifier has not been investigated, particularly for proteomic applications. This work compares the separation metrics of mobile phases modified with FA and FA/AF and explores the use of FA/AF for the LC-MS analysis of tryptic digests. Standard tryptic-digest peptides were used for comparative analysis of peak capacity and sample load tolerance. The compatibility of FA/AF in proteomic applications was examined with the analysis of soluble proteins from canine prostate carcinoma tissue. Overall, the use of FA/AF improved online RP-LC separations and led to significant increases in peptide identifications with improved protein sequence coverage.

The influence of anthropogenic shoreline changes on the littoral abundance of fish species in German lowland lakes varying in depth as determined by boosted regression trees

Residential development on lake shores is regularly associated with the conversion of natural littoral habitats to riprap, sheet piles, beaches, parks, or marinas. The subsequent loss of littoral vegetation induces a decline of structural diversity and impacts littoral fish communities. These impacts may be shaped by lake morphology. Using boosted regression trees (BRT) to relate fish abundance data from 57 north-east German lowland lakes to various factors characterizing trophic state, lake morphology, and shoreline development, we investigated the response of 11 fish species to shoreline development. The analyses revealed that mean depth followed by trophic level and shoreline length (SL) contributed most in explaining littoral fish abundance. BRT models built for deep and shallow lakes separately confirmed that primarily trophic level and SL influenced fish abundance but that littoral vegetation was relatively more important in deep compared to shallow lakes, indicating that the effects of shoreline development may be more pronounced in deep lakes where the littoral makes up a smaller proportion of the lake area as compared to shallow lakes. The BRT further demonstrated species-specific responses to shoreline degradation, indicating that the reliability of ecological quality assessments of lakes can be improved by applying separate metrics for individual species.

Using twitter to examine smoking behavior and perceptions of emerging tobacco products.

BackgroundSocial media platforms such as Twitter are rapidly becoming key resources for public health surveillance applications, yet little is known about Twitter users’ levels of informedness and sentiment toward tobacco, especially with regard to the emerging tobacco control challenges posed by hookah and electronic cigarettes.ObjectiveTo develop a content and sentiment analysis of tobacco-related Twitter posts and build machine learning classifiers to detect tobacco-relevant posts and sentiment towards tobacco, with a particular focus on new and emerging products like hookah and electronic cigarettes.MethodsWe collected 7362 tobacco-related Twitter posts at 15-day intervals from December 2011 to July 2012. Each tweet was manually classified using a triaxial scheme, capturing genre, theme, and sentiment. Using the collected data, machine-learning classifiers were trained to detect tobacco-related vs irrelevant tweets as well as positive vs negative sentiment, using Naïve Bayes, k-nearest neighbors, and Support Vector Machine (SVM) algorithms. Finally, phi contingency coefficients were computed between each of the categories to discover emergent patterns.ResultsThe most prevalent genres were first- and second-hand experience and opinion, and the most frequent themes were hookah, cessation, and pleasure. Sentiment toward tobacco was overall more positive (1939/4215, 46% of tweets) than negative (1349/4215, 32%) or neutral among tweets mentioning it, even excluding the 9% of tweets categorized as marketing. Three separate metrics converged to support an emergent distinction between, on one hand, hookah and electronic cigarettes corresponding to positive sentiment, and on the other hand, traditional tobacco products and more general references corresponding to negative sentiment. These metrics included correlations between categories in the annotation scheme (phihookah-positive=0.39; phie-cigs-positive=0.19); correlations between search keywords and sentiment (χ2 4=414.50, P<.001, Cramer’s V=0.36), and the most discriminating unigram features for positive and negative sentiment ranked by log odds ratio in the machine learning component of the study. In the automated classification tasks, SVMs using a relatively small number of unigram features (500) achieved best performance in discriminating tobacco-related from unrelated tweets (F score=0.85).ConclusionsNovel insights available through Twitter for tobacco surveillance are attested through the high prevalence of positive sentiment. This positive sentiment is correlated in complex ways with social image, personal experience, and recently popular products such as hookah and electronic cigarettes. Several apparent perceptual disconnects between these products and their health effects suggest opportunities for tobacco control education. Finally, machine classification of tobacco-related posts shows a promising edge over strictly keyword-based approaches, yielding an improved signal-to-noise ratio in Twitter data and paving the way for automated tobacco surveillance applications.

Performance Metrics of Speed and Separation Monitoring in Shared Workspaces

A set of metrics is proposed that evaluates speed and separation monitoring efficacy in industrial robot environments in terms of the quantification of safety and the effects on productivity. The collision potential is represented by separation metrics and sensor uncertainty based on perceived noise and bounding region radii. In the event of a bounding region collision between a robot and an obstacle during algorithm evaluation, the severity of the separation failure is reported as a percentage of volume penetration.

Algorithm for automatic analysis of electro-oculographic data.

BackgroundLarge amounts of electro-oculographic (EOG) data, recorded during electroencephalographic (EEG) measurements, go underutilized. We present an automatic, auto-calibrating algorithm that allows efficient analysis of such data sets.MethodsThe auto-calibration is based on automatic threshold value estimation. Amplitude threshold values for saccades and blinks are determined based on features in the recorded signal. The performance of the developed algorithm was tested by analyzing 4854 saccades and 213 blinks recorded in two different conditions: a task where the eye movements were controlled (saccade task) and a task with free viewing (multitask). The results were compared with results from a video-oculography (VOG) device and manually scored blinks.ResultsThe algorithm achieved 93% detection sensitivity for blinks with 4% false positive rate. The detection sensitivity for horizontal saccades was between 98% and 100%, and for oblique saccades between 95% and 100%. The classification sensitivity for horizontal and large oblique saccades (10 deg) was larger than 89%, and for vertical saccades larger than 82%. The duration and peak velocities of the detected horizontal saccades were similar to those in the literature. In the multitask measurement the detection sensitivity for saccades was 97% with a 6% false positive rate.ConclusionThe developed algorithm enables reliable analysis of EOG data recorded both during EEG and as a separate metrics.