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Stride-to-stride fluctuations during walking reflect age-related changes in gait adaptability and are estimated with nonlinear measures that confine data collection to controlled settings. Smartphones, with their embedded accelerometers, may provide accessible gait analysis throughout the day. This study investigated age-related differences in linear and nonlinear gait measures estimated from a smartphone accelerometer (SPAcc) in an unconstrained, free-living environment. Thirteen young adults (YA) and 11 older adults (OA) walked within a shopping mall with a SPAcc placed in their front right pants pocket. The inter-stride interval, calculated as the time difference between ipsilateral heel contacts, was used for dependent measures calculations. One-way repeated-measures analysis of variance revealed significant (p < 0.05) age-related differences (mean: YA, OA) for stride-time standard deviation (0.04 s, 0.05 s) and coefficient of variation (3.47%, 4.16%), sample entropy (SaEn) scale 1 (1.70, 1.86) and scale 3 (2.12, 1.80), and statistical persistence decay (31 strides, 23 strides). The fractal scaling index was not different between groups (0.93, 0.95), but exceeded those typically found in controlled settings, suggesting an upregulation in adaptive behaviour likely to accommodate the increased challenge of free-living walking. These findings support the SPAcc as a viable telehealth instrument for remote monitoring of gait dynamics, with implications for unsupervised fall-risk assessment.

Stride intervals in human walking fluctuate from one stride to the next, exhibiting statistical persistence. This statistical property is changed by aging, neural disorders, and experimental interventions. It has been hypothesized that the central nervous system is responsible for the statistical persistence. Human walking is a complex phenomenon generated through the dynamic interactions between the central nervous system and the biomechanical system. It has also been hypothesized that the statistical persistence emerges through the dynamic interactions during walking. In particular, a previous study integrated a biomechanical model composed of seven rigid links with a central pattern generator (CPG) model, which incorporated a phase resetting mechanism as sensory feedback as well as feedforward, trajectory tracking, and intermittent feedback controllers, and suggested that phase resetting contributes to the statistical persistence in stride intervals. However, the essential mechanisms remain largely unclear due to the complexity of the neuromechanical model. In this study, we reproduced the statistical persistence in stride intervals using a simplified neuromechanical model composed of a simple compass-type biomechanical model and a simple CPG model that incorporates only phase resetting and a feedforward controller. A lack of phase resetting induced a loss of statistical persistence, as observed for aging, neural disorders, and experimental interventions. These mechanisms were clarified based on the phase response characteristics of our model. These findings provide useful insight into the mechanisms responsible for the statistical persistence of stride intervals in human walking.

Human identification by fingerprints is based on the fundamental premise that ridge patterns from distinct fingers are different (uniqueness) and a fingerprint pattern does not change over time (persistence). Although the uniqueness of fingerprints has been investigated by developing statistical models to estimate the probability of error in comparing two random samples of fingerprints, the persistence of fingerprints has remained a general belief based on only a few case studies. In this study, fingerprint match (similarity) scores are analyzed by multilevel statistical models with covariates such as time interval between two fingerprints in comparison, subject’s age, and fingerprint image quality. Longitudinal fingerprint records of 15,597 subjects are sampled from an operational fingerprint database such that each individual has at least five 10-print records over a minimum time span of 5 y. In regard to the persistence of fingerprints, the longitudinal analysis on a single (right index) finger demonstrates that ( i ) genuine match scores tend to significantly decrease when time interval between two fingerprints in comparison increases, whereas the change in impostor match scores is negligible; and ( ii ) fingerprint recognition accuracy at operational settings, nevertheless, tends to be stable as the time interval increases up to 12 y, the maximum time span in the dataset. However, the uncertainty of temporal stability of fingerprint recognition accuracy becomes substantially large if either of the two fingerprints being compared is of poor quality. The conclusions drawn from 10-finger fusion analysis coincide with the conclusions from single-finger analysis.

Animal tracking data are indispensable for understanding the ecology, behaviour and physiology of mobile or cryptic species. Meaningful signals in these data can be obscured by noise due to imperfect measurement technologies, requiring rigorous quality control as part of any comprehensive analysis. State–space models are powerful tools that separate signal from noise. These tools are ideal for quality control of error‐prone location data and for inferring where animals are and what they are doing when they record or transmit other information. However, these statistical models can be challenging and time‐consuming to fit to diverse animal tracking data sets. The R package aniMotum eases the tasks of conducting quality control on and inference of changes in movement from animal tracking data. This is achieved via: (1) a simple but extensible workflow that accommodates both novice and experienced users; (2) automated processes that alleviate complexity from data processing and model specification/fitting steps; (3) simple movement models coupled with a powerful numerical optimization approach for rapid and reliable model fitting. We highlight aniMotum's capabilities through three applications to real animal tracking data. Full R code for these and additional applications is included as Supporting Information, so users can gain a deeper understanding of how to use aniMotum for their own analyses.

A record 1.2 million ha burned in British Columbia, Canada's extreme wildfire season of 2017. Key factors in this unprecedented event were the extreme warm and dry conditions that prevailed at the time, which are also reflected in extreme fire weather and behavior metrics. Using an event attribution method and a large ensemble of regional climate model simulations, we show that the risk factors affecting the event, and the area burned itself, were made substantially greater by anthropogenic climate change. We show over 95% of the probability for the observed maximum temperature anomalies is due to anthropogenic factors, that the event's high fire weather/behavior metrics were made 2–4 times more likely, and that anthropogenic climate change increased the area burned by a factor of 7–11. This profound influence of climate change on forest fire extremes in British Columbia, which is likely reflected in other regions and expected to intensify in the future, will require increasing attention in forest management, public health, and infrastructure. Plain Language Summary A record 1.2 million ha burned in British Columbia, Canada's extreme wildfire season of 2017. Key factors in this unprecedented event were the extreme warm and dry conditions that prevailed at the time, which are also reflected in extreme fire weather and behavior metrics. To quantify the influence of human‐induced climate change on this event, we compare the likelihood of the risk factors affecting the extreme fire season to an estimate of what the likelihood might have been without the human component. We find that human‐induced climate change contributed greatly to the probability of the observed extreme warm temperatures, high wildfire risk, and large burned areas. Key Points An event attribution analysis is performed for the record‐breaking wildfire season of 2017 in BC Anthropogenic climate change greatly increased the likelihood of extreme warm temperatures and high fire risk A strong anthropogenic climate change contribution is also found for the large area burned

Using a unique dataset of 44 Massive Open Online Courses (MOOCs), this article examines critical patterns of enrollment, engagement, persistence, and completion among students in online higher education. By leveraging fixed-effects specifications based on over 2.1 million student observations across more than 2,900 lectures, we analyzed engagement, persistence, and completion rates at the student, lecture, and course levels. We found compelling and consistent temporal patterns: across all courses, participation declines rapidly in the first week but subsequently flattens out in later weeks of the course. However, this decay is not entirely uniform. We also found that several student and lecture-specific traits were associated with student persistence and engagement. For example, the sequencing of a lecture within a batch of released videos as well as its title wording were related to student watching. We also saw consistent patterns in how student characteristics are associated with persistence and completion. Students were more likely to complete the course if they completed a pre-course survey or followed a quantitative track (as opposed to qualitative or auditing track) when available. These findings suggest potential course design changes that are likely to increase engagement, persistence, and completion in this important, new educational setting.

Aim To test if physiological acclimation can buffer species against increasing extreme heat due to climate change. Location Global. Time period 1960 to 2015. Major taxa studied Amphibians, arthropods, brachiopods, cnidarians, echinoderms, fishes, molluscs, reptiles. Methods We draw together new and existing data quantifying the warm acclimation response in 319 species as the acclimation response ratio (ARR): the increase in upper thermal limit per degree increase in experimental temperature. We develop worst‐case scenario climate projections to calculate the number of years and generations gained by ARR until loss of thermal safety. We further compute a vulnerability score that integrates across variables estimating exposure to climate change and species‐specific tolerance through traits, including physiological plasticity, generation time and latitudinal range extent. Results ARR is highly variable, but with marked differences across taxa, habitats and latitude. Polar terrestrial arthropods show high ARRs [95% upper confidence limit (UCL95%) = 0.68], as do some polar aquatic invertebrates that were acclimated for extended durations (ARR > 0.4). While this physiological plasticity buys 100s of years until thermal safety is lost, combination with long generation times leads to decreased potential for evolutionary adaptation. Additionally, 27% of marine polar invertebrates have no capacity for acclimation and reptiles and amphibians have minimal ARR (UCL95% = 0.16). Low physiological plasticity, long generations times and restricted latitudinal ranges combine to distinguish reptiles, amphibians and polar invertebrates as being highly vulnerable amongst ectotherms. Main conclusions In some taxa the combined effects of acclimation capacity and generation time can provide 100s of years and generations before thermal safety is lost. The accuracy of assessments of vulnerability to climate change will be improved by considering multiple aspects of species’ biology that, in combination may increase persistence under extreme heat events, and increase the probability for evolutionary rescue.

The Medication Adherence Risk Score (MARS) is a calculated score using pharmacy transactional data spanning 50% of the South African private pharmacy market. This study aims to demonstrate that the existing MARS model enhances risk stratification by identifying individuals at increased risk of mortality related to non-adherence to chronic medication. This was a retrospective cohort study in which an analysis of the relative mortality experience was compared to a standard fully underwritten base was performed for each of the MARS categories (low, medium, high and very high). The actual-to-expected ratio (AER) and relative risk (RR) for each category were compared across age groups and gender. The least absolute shrinkage and selection operator (LASSO) regression analysis method was applied to determine the most important variables within the dataset, providing insight into whether MARS offered more benefit than traditional risk rating factors. A time-to-event analysis by MARS categories was performed using the Cox proportional hazards model. The mortality experience of the study population was higher than the expected fully underwritten base (AER = 175%). For the overall sample, increasing AER and RR did not correlate with increasing MARS categories. However, use of the MARS in addition to age band allowed for differentiation of risk within the 25 to 55 age bands, with a higher MARS score indicating a higher AER and RR. The time-to-event analysis showed a statistically significant difference in the mean number of months before death occurred between the different MARS categories (low = 26.53; medium = 8.93; high = 7.02; very high = 6.92; p < 0.001). The MARS is not generalisable across all groups, as evidenced by the absence of a monotonic trend in the overall sample. However, when combined with age, it effectively differentiated mortality risk for individuals aged 25-55. The standard fully underwritten model underestimated the number of deaths within this pharmacy population. The time-to-event analysis showed a significant inverse relationship between MARS category and survival time.

A systematic review was conducted to produce an up-to-date and comprehensive summary of qualitative and quantitative evidence specific to the factors related to undergraduate Latina/o student academic success outcomes during college. The purpose of the study was to make sense of and provide critique to this rapidly growing body of research, as well as to direct future research efforts. Findings indicate that a combination of (a) sociocultural characteristics; (b) academic self-confidence; (c) beliefs, ethnic/racial identity, and coping styles; (d) precollege academic experiences; (e) college experiences; (f) internal motivation and commitment; (g) interactions with supportive individuals; (h) perceptions of the campus climate/environment; and (i) institutional type/characteristics are related to one or more academic success outcomes for Latina/o students. The article concludes with specific recommendations including the use of additional methods, frameworks and perspectives that we hope will be useful in advancing this line of work.

Despite a surge of empirical work on student participation in online learning environments, the causal links between the learning-related factors and processes with the desired learning outcomes remain unexplored. This study presents a systematic literature review of approaches to model learning in Massive Open Online Courses offering an analysis of learning-related constructs used in the prediction and measurement of student engagement and learning outcome. Based on our literature review, we identify current gaps in the research, including a lack of solid frameworks to explain learning in open online setting. Finally, we put forward a novel framework suitable for open online contexts based on a well-established model of student engagement. Our model is intended to guide future work studying the association between contextual factors (i.e., demographic, classroom, and individual needs), student engagement (i.e., academic, behavioral, cognitive, and affective engagement metrics), and learning outcomes (i.e., academic, social, and affective). The proposed model affords further interstudy comparisons as well as comparative studies with more traditional education models.