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Background: Modern biologging technologies allow researchers to gain a better understanding of animal movements, offering opportunities to measure survival and remotely study the breeding success of wild birds, i.e., by locating nests. This is particularly useful for species whose nests are difficult to find or access, or when disturbances can impact the breeding outcome. We developed and validated, with field data, a framework to detect nesting events by two sandgrouse species, the black-bellied (Pterocles orientalis) and pin-tailed sandgrouse (Pterocles alchata), using GPS and Overall Dynamic Body Acceleration (ODBA) data. Sandgrouses are ground-nesting, cryptic, and elusive birds with biparental incubation efforts. Because both sexes take turns to incubate, a novel framework considering when tagged individuals are on incubation duty or not needs to be designed to detect nests. Results: We tagged 52 birds with high-resolution GPS devices to monitor their breeding during 2021–24. Using remote tracking and field data from the first 2 years (2021–22), we first determined sex-specific time windows for incubation to maximise differentiation between incubation and non-incubation behaviours. We then used a threshold-based classification to identify incubation days and inferred the minimum number of successive incubation days needed to correctly identify a nesting event. We show how ODBA and GPS data can be used to successfully detect nests incubated for only 2 or 3 days. GPS-only data or combined GPS-ODBA data had a success rate of around 95%, whereas ODBA-only data had a success rate of 100%. Cross-validation using data from 2023 to 2024 confirmed the model's performance, showing an overall success > 90% for GPS-only and ODBA-only data and of 85% for combined GPS–ODBA data. Conclusions: By accurately identifying nesting events, our framework offers new opportunities to study the breeding of conservation-dependent species. Besides its applicability to ground-nesting species with biparental care and sex-specific incubation schedules, the framework can be adapted to other bird species sensitive to disturbances or with inaccessible nesting sites. By doing so, it reduces the need for nest visits and associated disturbances, as well as the carbon footprint and expenses associated with fieldwork. [ABSTRACT FROM AUTHOR]

Biodiversity is critical for maintaining ecosystem function but is threatened by increasing anthropogenic pressures. In the Southern Ocean, a highly biologically productive region containing many endemic species, proactive management is urgently needed to mitigate increasing pressures from fishing, climate change, and tourism. Site‐based conservation is one important tool for managing the negative impacts of human activities on ecosystems. The Key Biodiversity Area (KBA) Standard is a standardized framework used to define sites vital for the persistence of global biodiversity based on criteria and quantitative thresholds. We used tracking data from 14 species of Antarctic and subantarctic seabirds and pinnipeds from the publicly available Retrospective Analysis of Antarctic Tracking Data (RAATD) data set to define KBAs for a diverse suite of marine predators. We used track2kba, an R package that supports identification of KBAs from telemetry data through identification of highly used habitat areas and estimates of local abundance within sites. We compared abundance estimates at each site with thresholds for KBA criteria A1, B1, and D1 (related to globally threatened species, individual geographically restricted species, and demographic aggregations, respectively). We identified 30 potential KBAs for 13 species distributed throughout the Southern Ocean that were vital for each individual species, population, and life‐history stage for which they were determined. These areas were identified as highly used by these populations based on observational data and complement the ongoing habitat modeling and bioregionalization work that has been used to prioritize conservation areas in this region. Although further work is needed to identify potential KBAs based on additional current and future data sets, we highlight the benefits of utilizing KBAs as part of a holistic approach to marine conservation, given their significant value as a global conservation tool. [ABSTRACT FROM AUTHOR]

Ongoing technological advances have led to a rapid increase in the number, type and scope of animal‐tracking studies. In response, many software tools have been developed to analyse animal movement data. These tools generally focus on movement modelling, but the steps required to clean raw data files from different tracking devices have been largely ignored. Such pre‐processing steps are often time‐consuming and involve a steep learning curve but are crucial for the creation of high‐quality, standardised and shareable data. Moreover, decisions made at this early stage can substantially influence subsequent analyses, and in the current age of reproducibility crisis, the transparency of this process is vital.Here we present an open‐access, reproducible toolkit written in the programming language R for processing raw data files into a single cleaned data set for analyses and upload to online tracking databases (found here: https://github.com/ExMove/ExMove). The toolkit comprises well‐documented and flexible code to facilitate data processing and user understanding, both of which can increase user confidence and improve the uptake of sharing open and reproducible code. Additionally, we provide an overview website (found here: https://exmove.github.io/) and a Shiny app to help users visualise tracking data and assist with parameter determination during data cleaning.The toolkit is generalisable to different data formats and device types, uses modern 'tidy coding' practices, and relies on a few well‐maintained packages. Among these, we perform spatial manipulations using the package sf.Overall, by collating all required steps from data collection to archiving on open access databases into a single, robust pipeline, our toolkit provides a valuable resource for anyone conducting animal movement analyses and represents an important step towards increased standardisation and reproducibility in animal movement ecology. [ABSTRACT FROM AUTHOR]

Wild boars (Sus scrofa Linnaeus) damage human activities by invading farmlands and farms in South Korea. Recently, research has been conducted to predict their mobility to prevent damage. This study predicted the movement of wild boars in Jinju-si (712.95 km2), South Korea. Home range analysis and point density analysis were conducted with the collected wild boar coordinate data, and two cost scenarios were constructed that were applied as weights in the basic resistance value (BR) based on previous studies. Using Circuitscape, the connectivity of wild boars' movement applied with the two scenarios was predicted and the results were compared. As a result of home range analysis, wild boars primarily utilized forests compared to other land types. In the result of point density, only forest and grass were identified in C5, which had the highest density. As a result of connectivity analysis, the average current density of Scenario 1 was 0.75 (max = 2.52, min = 0.20), and the average current density of Scenario 2 was 0.75 (max = 2.26, min = 0.25). The visual differences in the results of the two scenarios were not significant. However, upon closer inspection of enlarged areas, discrepancies were observed, particularly around grass near water and grass surrounding used areas. Utilizing actual wild boar data, we identified the movement routes of wild boars, enhancing the ability to predict areas where they may appear. This study can be used as basic data when preparing damage prevention measures or establishing wild boar management plans in areas where wild boars are expected to appear. [ABSTRACT FROM AUTHOR]

IntroductionThis study examined the relationship between ball release points and pitching performance among professional baseball pitchers, with a focus on variability.MethodsWe used open-source data to compare ball release point variability between Major League Baseball (MLB) and Minor League Baseball (MiLB) players. The relationship between pitching performance and variability was analyzed using multiple regression analysis.ResultsMLB players exhibited smaller ball release point variability compared to MiLB players. The analysis showed that pitching performance was strongly related to ball release point variability, especially in the horizontal direction on the coronal plane. Horizontal ball release point variability was most strongly related to strikeout ability among pitching performances.DiscussionThese results suggest that reducing horizontal ball release point variability may improve pitching performance, particularly by increasing strikeouts and reducing home runs allowed. This study provides a data-driven approach to understanding the mechanics of pitching and can be applied to the development of advanced training methods and technical solutions aimed at improving pitching performance in baseball players.

Abstract Automated positioning devices can generate large datasets with information on the movement of humans, animals and objects, revealing patterns of movement, hot spots and overlaps among others. However, in the case of Automated Information Systems (AIS), attached to vessels, observed strange behaviors in the tracking datasets may come from intentional manipulation of the electronic devices. Thus, the analysis of anomalies can provide valuable information on suspicious behavior. Here, we analyze anomalies of fishing vessel trajectories obtained with the Automatic Identification System. The map of silent anomalies, those that occur when positioning data are absent for more than 24 hours, shows that they are most likely to occur closer to land, with 87.1% of anomalies observed within 100 km of the coast. This behavior suggests the potential of identifying silence anomalies as a proxy for illegal activities. With the increasing availability of high-resolution positioning of vessels and the development of powerful statistical analytical tools, we provide hints on the automatic detection of illegal activities that may help optimize the management of fishing resources.

Since gaining popularity via the Moneyball story near the turn of the century, data analytics has become a common feature of high-level baseball. In recent years, optical tracking technologies have allowed data to move beyond basic box-score measures of performance (e.g., strikeouts and batting averages) to high-detail measures of ball movement following a pitch (e.g., spin rates, release speeds, and ending locations) or hit (e.g., exit velocity and launch angle). Such information has provided analysts with new ways to measure the game and communicate complex results via helpful visualizations. The data described in this article were recorded by an optical tracking unit stationed on a collegiate baseball field over a series of 21 exhibition matches. The technology was monitored by a trained individual who confirmed, cleaned, and compiled the data after every game. Each observation (n = 3,344) corresponds to a pitch that was thrown during a game, and the associated columns provide a variety of descriptive information related to the pitch and its eventual outcome. Data of this detail at the collegiate level of play can be difficult to acquire, and the high level of granularity allows for a variety of analyses and visualizations to be conducted for baseball and non-baseball purposes.

AbstractBackgroundMethodResultsConclusionMost previous studies on the distribution of gambling losses were based on self-reported data. In this study, we employed tracking data (i.e. electronic betting records) to examine the concentration of gambling losses and whether concentration varies by product type.Tracking data were provided by the Norwegian gambling monopolist, Norsk Tipping (NT). Data comprised of 14 different games for a random draw of 2% (N = 39 995) of all NT’s customers in 2019. We applied three measures of concentration of gambling losses: the mean to median ratio, the Gini coefficient, and the proportion of total losses accounted for by the upper 1%, 5% or 10% of those who gamble.Across the 14 games, the mean/median ratio was 2.22, ranging from 1.37 to 17.48 for the different games, whereas the overall Gini coefficient was 0.65, ranging from 0.55 to 0.90. The upper 1%, 5% and 10% of those who gamble accounted for 17.9% (range = 5.6 − 3 8.3%), 39.5% (range = 23.6 − 74.3%), and 52.2% (range = 37.9 − 86.9%) of the losses, respectively. High concentration of losses was especially pronounced for one type of lottery (Keno), two online casino games (KongKasino and Bingoria), and for two sports betting games (Oddsen and Tipping). These findings were consistent across measures.Overall, the results lend strong support to the notion that a disproportionately large fraction of gambling losses are accounted for by a relatively small minority of people and that concentration of losses varies by product type. [ABSTRACT FROM AUTHOR]

Automated positioning devices can generate large datasets with information on the movement of humans, animals and objects, revealing patterns of movement, hot spots and overlaps among others. However, in the case of Automated Information Systems (AIS), attached to vessels, observed strange behaviors in the tracking datasets may come from intentional manipulation of the electronic devices. Thus, the analysis of anomalies can provide valuable information on suspicious behavior. Here, we analyze anomalies of fishing vessel trajectories obtained with the Automatic Identification System. The map of silent anomalies, those that occur when positioning data are absent for more than 24 hours, shows that they are most likely to occur closer to land, with 87.1% of anomalies observed within 100 km of the coast. This behavior suggests the potential of identifying silence anomalies as a proxy for illegal activities. With the increasing availability of high-resolution positioning of vessels and the development of powerful statistical analytical tools, we provide hints on the automatic detection of illegal activities that may help optimize the management of fishing resources. [ABSTRACT FROM AUTHOR]

This paper explores defensive play in soccer. The analysis is predicated on the assumption that the area of the convex hull formed by the players on a team provides a proxy for defensive style where small areas coincide with a greater defensive focus. With the availability of tracking data, the massive dataset considered in this paper consists of areas of convex hulls, related covariates and shots taken during matches. Whereas the pre-processing of the data is an exercise in data science, the statistical analysis is carried out using linear models. The resultant messages are nuanced but the primary message suggests that an extreme defensive style (defined by a small convex hull) is negatively associated with generating shots. [ABSTRACT FROM AUTHOR]

In recent years, the game of football has made a shift towards being more quantitative. With the advent of charting and tracking data, player evaluation is able to be studied from several different angles. In this paper, we build and refine two novel metrics: Bite Distance Under Expected (BDUE) and Ground Covered Over Expected (GCOE) for the evaluation of linebackers in the National Football League (NFL). Here, we show that these metrics are heavily correlated with each other, which demonstrates the trade-off linebackers have to make between being aggressive against the run and being effective when the opposing offense is using play-action. We also show that these metrics are more stable than those in the public space. Finally, we show how these metrics measure deception by opposing offenses. [ABSTRACT FROM AUTHOR]

Driven by recent advances in technology, tracking devices allow to collect high-frequency data on the position of players in (association) football matches and in many other sports. Although such data sets are available to every professional team, most teams still rely on time-consuming video analysis when analysing future opponents, for example with regard to how goals were scored or a team's general style of play. In this contribution, we provide a data-driven approach for automated classification of tactics in football. For that purpose, we consider hidden Markov models (HMMs) to analyse high-frequency tracking data, where the underlying states serve for a team's tactic. In particular, as space control in football has been considered a major driver of success, we focus on the effective playing space, which is the convex hull created by the players excluding the goalkeeper. This quantity relates to both playing style and team behavior. Using copula-based HMMs, we model jointly the effective playing space of both teams to account for the competitive nature of the game. Our model thus provides an estimate of a team's playing style at each time point, which can be beneficial for team managers but also of huge interest to football fans. [ABSTRACT FROM AUTHOR]

The conservation of long‐distance migratory birds requires coordination between the multiple countries connected by the movements of these species. The recent expansion of tracking studies is shedding new light on these movements, but much of this information is fragmented and inaccessible to conservation practitioners and policy makers. We synthesized current knowledge on the connectivity established between countries by landbirds and raptors migrating along the African–Eurasian flyway. We reviewed tracking studies to compile migration records for 1229 individual birds, from which we derived 544 migratory links, each link corresponding to a species' connection between a breeding country in Europe and a nonbreeding country in sub‐Saharan Africa. We used these migratory links to analyze trends in knowledge over time and spatial patterns of connectivity per country (across species), per species (across countries), and at the flyway scale (across all countries and all species). The number of tracking studies available increased steadily since 2010 (particularly for landbirds), but the coverage of existing tracking data was highly incomplete. An average of 7.5% of migratory landbird species and 14.6% of raptor species were tracked per country. More data existed from central and western European countries, and it was biased toward larger bodied species. We provide species‐ and country‐level syntheses of the migratory links we identified from the reviewed studies, involving 123 populations of 43 species, migrating between 28 European and 43 African countries. Several countries (e.g., Spain, Poland, Ethiopia, Democratic Republic of Congo) are strategic priorities for future tracking studies to complement existing data, particularly on landbirds. Despite the limitations in existing tracking data, our data and results can inform discussions under 2 key policy instruments at the flyway scale: the African–Eurasian Migratory Landbirds Action Plan and the Memorandum of Understanding on the Conservation of Migratory Birds of Prey in Africa and Eurasia. [ABSTRACT FROM AUTHOR]

The red fox (Vulpes vulpes) is one of the most adaptable carnivorans, thriving in cities across the globe. We used GPS-tracking of five suburban foxes across high-density residential suburbs of Perth, Western Australia to quantify (1) their habitat selection and (2) home range area. All five foxes showed statistically significant avoidance of residential locations (p < 0.001) and preference for parkland (p < 0.001), with native vegetation reserves, golf courses, and water reserves showing disproportionately greater use. Landuse category also influenced their movements, with foxes moving quickest (i.e., commuting) in proximity to roads and slowest (i.e., foraging) when they were further from roads. Three females had core home ranges (50% autocorrelated-corrected kernel density estimate; AKDEc) averaging 37 ± 20 ha or 95% AKDEc averaging 208 ± 196 ha. One male had a 95 ha core home range and 349 ha 95% AKDEc but the other male covered an area ~ 20 times this: using a 371 ha core home range and 7,368 ha 95% AKDEc. The extensive movement patterns we describe are likely to be common for urban foxes, with half of published home range estimates for urban foxes (principally based on VHF data) excluding data for 'lost' individuals or animals that showed 'excursions'. It is likely that the home range estimates for these urban exploiters have therefore been grossly underestimated to date. Further application of GPS trackers that allow remote download will vastly improve our understanding of habitat preference and exploitation of resources by urban foxes. [ABSTRACT FROM AUTHOR]

The growing field of movement ecology uses high resolution movement data to analyze animal behavior across multiple scales: from individual foraging decisions to population-level space-use patterns. These analyses contribute to various subfields of ecology-inter alia behavioral, disease, landscape, resource, and wildlife-and facilitate facilitate novel exploration in fields ranging from conservation planning to public health. Despite the growing availability and general accessibility of animal movement data, much potential remains for the analytical methods of movement ecology to be incorporated in all types of geographic analyses. This review provides for the Geographical Information Sciences (GIS) community an overview of the most common movement metrics and methods of analysis employed by animal ecologists. Through illustrative applications, we emphasize the potential for movement analyses to promote transdisciplinary GIS/wildlife-ecology research.

Older adults are increasingly using smartphones. Researchers have identified demographic, attitudinal factors and social outcomes related to inequalities in access, skills, and activities of internet use. While this research has been ongoing for years, studies about the digital inequalities related to the use of smartphones by older adults are still scarce, and they rarely have undertaken sociocultural comparisons of usage. This article builds on an analysis of tracking data of older adults' (pp. 55–79) smartphone use, to explore digital inequalities around traditional axes of inequality and sociocultural factors that shape older adults' smartphone use. It adds a comparative perspective for understanding emerging inequalities related to sociocultural contexts to the existing literature. The results show that differences cannot be explained solely by sociodemographic factors, but also possibly by national policies and culture. Particularly, age, on its own, is not able to explain the differences reported. Older adults engage in a range of different activities on their smartphones, and differences between groups across the traditional axes of inequalities do not mirror exactly the differences found in previous research on internet use. This is important considering the risk of emerging new inequalities and the ongoing revisiting of policies to mitigate them. [ABSTRACT FROM AUTHOR]

Since gaining popularity via the Moneyball story near the turn of the century, data analytics has become a common feature of high-level baseball. In recent years, optical tracking technologies have allowed data to move beyond basic box-score measures of performance (e.g., strikeouts and batting averages) to high-detail measures of ball movement following a pitch (e.g., spin rates, release speeds, and ending locations) or hit (e.g., exit velocity and launch angle). Such information has provided analysts with new ways to measure the game and communicate complex results via helpful visualizations. The data described in this article were recorded by an optical tracking unit stationed on a collegiate baseball field over a series of 21 exhibition matches. The technology was monitored by a trained individual who confirmed, cleaned, and compiled the data after every game. Each observation ( n = 3,344) corresponds to a pitch that was thrown during a game, and the associated columns provide a variety of descriptive information related to the pitch and its eventual outcome. Data of this detail at the collegiate level of play can be difficult to acquire, and the high level of granularity allows for a variety of analyses and visualizations to be conducted for baseball and non-baseball purposes., (© 2024 The Author(s).)

The paper presents a computational approach to Availability of soccer players. Availability is defined as the probability that a pass reaches the target player without being intercepted by opponents. Clearly, a computational model for this probability grounds on models for ball dynamics, player movements, and technical skills of the pass giver. Our approach aggregates these quantities for all possible passes to the target player to compute a single Availability value. Empirically, our approach outperforms state-of-the-art competitors using data from 58 professional soccer matches. Moreover, our experiments indicate that the model can even outperform soccer coaches in assessing the availability of soccer players from static images. [ABSTRACT FROM AUTHOR]

Information on marine predator at‐sea distributions is key to understanding ecosystem and community dynamics and an important component of spatial management frameworks that aim to identify regions important for conservation. Tracking data from seabirds are widely used to define priority areas for conservation, but such data are often restricted to the breeding population. This also applies to penguins in Antarctica, where identification of important habitat for nonbreeders has received limited attention. Nonbreeding penguins are expected to have larger foraging distributions than breeding conspecifics, which may alter their interactions with physical environmental factors, conspecifics, other marine predators, and threats. We studied the movement behavior of nonbreeding Adélie penguins tracked during the 2016/2017 breeding season at King George Island in the South Shetland Islands, Antarctica. We quantify how nonbreeding penguins' horizontal moment behavior varies in relation to environmental conditions and assess the extent of spatial overlap in the foraging ranges of nonbreeders and breeders, which were tracked over several years. Nonbreeders increased their prey search and area‐restricted foraging behavior as sea surface temperature and bottom depths decreased, and in response to increasing sea ice concentration. Nonbreeders tended to transit (high directional movement) over the relatively deep Central Basin of the Bransfield Strait. The majority of foraging behavior occurred within the colder, Weddell Sea–sourced water of the Antarctic Coastal Current (incubation) and in the Weddell Sea (crèche). The utilization distributions of breeders and nonbreeders overlapped in the central Bransfield Strait. Spatial segregation was greater during the crèche stage of breeding compared to incubation and brood, because chick provisioning still constrained the foraging range of breeders to a scale of a few tens of kilometers, while nonbreeders commenced with premolt foraging trips into the Weddell Sea. Our results show that breeding and nonbreeding penguins may not be impacted similarly by local environmental variability, given that their spatial and temporal scales of foraging differ during some part of the austral summer. Our study highlights the need to account for different life history stages when characterizing foraging behavior of marine predator populations. This is particularly important for "sentinel" species monitored as part of marine conservation and ecosystem‐based management programs. [ABSTRACT FROM AUTHOR]

Modern tracking devices allow for the collection of high‐volume animal tracking data at improved sampling rates over very‐high‐frequency radiotelemetry. Home range estimation is a key output from these tracking datasets, but the inherent properties of animal movement can lead traditional statistical methods to under‐ or overestimate home range areas.The autocorrelated kernel density estimation (AKDE) family of estimators was designed to be statistically efficient while explicitly dealing with the complexities of modern movement data: autocorrelation, small sample sizes and missing or irregularly sampled data. Although each of these estimators has been described in separate technical papers, here we review how these estimators work and provide a user‐friendly guide on how they may be combined to reduce multiple biases simultaneously.We describe the magnitude of the improvements offered by these estimators and their impact on home range area estimates, using both empirical case studies and simulations, contrasting their computational costs.Finally, we provide guidelines for researchers to choose among alternative estimators and an R script to facilitate the application and interpretation of AKDE home range estimates. [ABSTRACT FROM AUTHOR]

Research on movement has increased over the past two decades, particularly in movement ecology, which studies animal movement. Taking context into consideration when analysing movement can contribute towards the understanding and prediction of behaviour. The only way for studying animal movement decision-making and their responses to environmental conditions is through analysis of ancillary data that represent conditions where the animal moves. In GIScience this is called Context-Aware Movement Analysis (CAMA). As ecology becomes more data-oriented, we believe that there is a need to both review what CAMA means for ecology in methodological terms and to provide reliable definitions that will bridge the divide between the content-centric and data-centric analytical frameworks. We reviewed the literature and proposed a definition for context, develop a taxonomy for contextual variables in movement ecology and discuss research gaps and open challenges in the science of movement more broadly. We found that the main research for CAMA in the coming years should focus on: 1) integration of contextual data and movement data in space and time, 2) tools that account for the temporal dynamics of contextual data, 3) ways to represent contextualized movement data, and 4) approaches to extract meaningful information from contextualized data. [ABSTRACT FROM AUTHOR]

The dynamic queries by wireless mobile network users, will generate the social data with location tags and tracking data sequences, which enable the adversary can infer privacy information combined background knowledge, especially strong geographic correlation information. Therefore, we propose the Geo-matching privacy inference attack methods based on road network and sensitive semantic location. To address these issues, a k-implicit data publishing scheme with adaptive privacy budget is presented, which is based on the road network topological graph and the sensitivity quantification of grid unit, and also realizes an optimized dynamic anonymous region construction. Finally, the proposed Geo-matching attack algorithm is simulated to verify the effectiveness of the k-implicit data publishing scheme. The experiment results show that the proposed scheme can better resist the Geomatching attack under different privacy budget thresholds. [ABSTRACT FROM AUTHOR]

As a case study, this paper proposes a method to detect improvements in dismantling processes through more efficient operation of vehicle dismantling machines for end-of-life vehicles. Using the tracking data collected by a sensor attached to the vehicle dismantling machine, the proposed method identifies processes to be learned and processes to be improved, compares measures for operating and planning skills between the two types of processes identified, and detects ways to improve the processes. The proposed method is applied to a case of dismantling works in a company, and the results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

We study the automatic annotation of situations in soccer games. At first sight, this translates nicely into a standard supervised learning problem. However, in a fully supervised setting, predictive accuracies are supposed to correlate positively with the amount of labeled situations: more labeled training data simply promise better performance. Unfortunately, non-trivially annotated situations in soccer games are scarce, expensive and almost always require human experts; a fully supervised approach appears infeasible. Hence, we split the problem into two parts and learn (i) a meaningful feature representation using variational autoencoders on unlabeled data at large scales and (ii) a large-margin classifier acting in this feature space but utilize only a few (manually) annotated examples of the situation of interest. We propose four different architectures of the variational autoencoder and empirically study the detection of corner kicks, crosses and counterattacks. We observe high predictive accuracies above 90% AUC irrespectively of the task.

Abstract Background Bar code- or radio frequency identification (RFID)-based medical instrument management systems have gradually been introduced in the field of surgical medicine for the individual management and identification of instruments. We hypothesized that individual management of instruments using RFID tags can provide previously unavailable information, particularly the precise service life of an instrument. Such information can be used to prevent medical accidents caused by surgical instrument failure. This study aimed to predict the precise service life of instruments by analyzing the data available in instrument management systems. Methods We evaluated the repair history of instruments and the usage count until failure and then analyzed the data by the following three methods: the distribution of the instrument usage count was determined, an instrument failure probability model was generated through logistic regression analysis, and survival analysis was performed to predict instrument failure. Results The usage count followed a normal distribution. Analysis showed that instruments were not used uniformly during surgery. In addition, the Kaplan–Meier curves plotted for five types of instruments showed significant differences in the cumulative survival rate of different instruments. Conclusions The usage history of instruments obtained with RFID tags or bar codes can be used to predict the probability of instrument failure. This prediction is significant for determining the service life of an instrument. Implementation of the developed model in instrument management systems can help prevent accidents due to instrument failure. Knowledge of the instrument service life will also help in developing a purchase plan for instruments to minimize wastage.

Sex differences in diet and foraging behaviour are common in sexually dimorphic species, often driven by differences in the cost of locomotion or ability to exploit different ecological niches. However, sex-specific foraging strategies also occur in monomorphic or slightly dimorphic species where the drivers are poorly understood. Here, we study sex differences in foraging of northern gannets (Morus bassanus), where females are only slightly heavier than males. Using concurrently tracked gannets (298 full foraging trips from 81 individuals) and fishing vessels across 5 years, we quantify individual-based vessel-associated putative foraging, and relate this to discard consumption. We found a significant positive relationship between time spent in vessel-associated foraging and discard consumption for both sexes. However, while females showed greater proportions of vessel-associated foraging than males, discarded fish contributed less to the diet of females in all years. These results contrast with previous suggestions that female gannets interact with vessels less often than males, and are consistent with competitive exclusion of females from trawler-associated discards. Our findings give insight into sexual differences in foraging behaviour in the absence of dimorphism that are necessary to predict their response to environmental and anthropogenic changes.

This study proposes an intelligent tracking method based on the expectation maximisation approach. To obtain the consistent tracking performance, the observer should maintain contact with the target and continuously update its tracking data. Unfortunately, a significant problem arises from the uncertainty of the target motion. The reasons behind this uncertainty include losses, time‐varying noises, external inputs, and others. Above all, a sharp manoeuvre by the arbitrary acceleration input mainly degrades the tracking performance. A variety of techniques were needed and studied to enhance performance levels. This study focuses on acceleration as the cause of the uncertainty and a consistent approximation of the acceleration, leading to highly efficient tracking performance. The EM makes it possible to separate the noise term from the acceleration inputs and the noise components. Finally, a manoeuvring submarine with three‐dimensional coordinates is provided as an example to show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The pervasive use of mobile information technologies brings new patterns of media usage, but also challenges to the measurement of media exposure. Researchers wishing to, for example, understand the nature of selective exposure on algorithmically driven platforms need to precisely attribute individuals' exposure to specific content. Prior research has used tracking data to show that survey-based self-reports of media exposure are critically unreliable. So far, however, little effort has been invested into assessing the specific biases of tracking methods themselves. Using data from a multimethod study, we show that tracking data from mobile devices is linked to systematic distortions in self-report biases. Further inherent but unobservable sources of bias, along with potential solutions, are discussed. [ABSTRACT FROM AUTHOR]

Understanding how citizens keep themselves informed about current affairs is crucial for a functioning democracy. Extant research suggests that in an increasingly fragmented digital news environment, search engines and social media platforms promote more incidental, but potentially more shallow modes of engagement with news compared to the act of routinely accessing a news organization's website. In this study, we examine classic predictors of news consumption to explain the preference for three modes of news engagement in online tracking data: routine news use, news use triggered by social media, and news use as part of a general search for information. In pursuit of this aim, we make use of a unique data set that combines tracking data with survey data. Our findings show differences in predictors between preference for regular (direct) engagement, general search-driven, and social media–driven modes of news engagement. In describing behavioral differences in news consumption patterns, we demonstrate a clear need for further analysis of behavioral tracking data in relation to self-reported measures in order to further qualify differences in modes of news engagement. [ABSTRACT FROM AUTHOR]

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The NFL collects detailed tracking data capturing the location of all players and the ball during each play. Although the raw form of this data is not publicly available, the NFL releases a set of aggregated statistics via their Next Gen Stats (NGS) platform. They also provide charts showing the locations of pass attempts and outcomes for individual quarterbacks. Our work aims to partially close the gap between what data is available privately (to NFL teams) and publicly, and our contribution is two-fold. First, we introduce an image processing tool designed specifically for extracting the raw data from the NGS pass charts. We extract the pass outcome, coordinates, and other metadata. Second, we analyze the resulting dataset, examining the spatial tendencies and performances of individual quarterbacks and defenses. We use a generalized additive model for completion percentages by field location. We introduce a naive Bayes approach for estimating the 2-D completion percentage surfaces of individual teams and quarterbacks, and we provide a one-number summary, completion percentage above expectation (CPAE), for evaluating quarterbacks and team defenses. We find that our pass location data closely matches the NFL's tracking data, and that our CPAE metric closely matches the NFL's proprietary CPAE metric. [ABSTRACT FROM AUTHOR]

Objective: We present a non-invasive wearable device for fertility monitoring and propose an effective and flexible statistical learning algorithm to detect and predict ovulation using data captured by this device. Methods: The system consists of an earpiece, which measures the ear canal temperature every 5 min during night sleep hours, and a base station that transmits data to a smartphone application for analysis. We establish a data-cleaning protocol for data preprocessing and then fit a Hidden Markov Model (HMM) with two hidden states of high and low temperature to identify the more probable state of each time point via the predicted probabilities. Finally, a post-processing procedure is developed to incorporate biorhythm information to form a time-course biphasic profile for each subject. Results: The performance of the proposed algorithms applied to data collected by the device are compared with traditional methods in terms of match rate with self-reported ovulation days confirmed with an ovulation test kit. Empirical study results from a group of 34 users yielded significant improvements over the traditional methods in terms of detection accuracy (with sensitivity 92.31%) and prediction power (23.07–31.55% higher). Conclusion: We demonstrated the feasibility for reliable ovulation detection and prediction with high-frequency temperature data collected by a non-invasive wearable device. Significance: Traditional fertility monitoring methods are often either inaccurate or inconvenient. The wearable device and learning algorithm presented in this paper provide a user friendly and reliable platform for tracking ovulation, which may have a broad impact on both fertility research and real-world family planning. [ABSTRACT FROM AUTHOR]