Detailed Motion Research Articles

Human Action Recognition and Note Recognition: A Deep Learning Approach Using STA-GCN.

Human action recognition (HAR) is growing in machine learning with a wide range of applications. One challenging aspect of HAR is recognizing human actions while playing music, further complicated by the need to recognize the musical notes being played. This paper proposes a deep learning-based method for simultaneous HAR and musical note recognition in music performances. We conducted experiments on Morin khuur performances, a traditional Mongolian instrument. The proposed method consists of two stages. First, we created a new dataset of Morin khuur performances. We used motion capture systems and depth sensors to collect data that includes hand keypoints, instrument segmentation information, and detailed movement information. We then analyzed RGB images, depth images, and motion data to determine which type of data provides the most valuable features for recognizing actions and notes in music performances. The second stage utilizes a Spatial Temporal Attention Graph Convolutional Network (STA-GCN) to recognize musical notes as continuous gestures. The STA-GCN model is designed to learn the relationships between hand keypoints and instrument segmentation information, which are crucial for accurate recognition. Evaluation on our dataset demonstrates that our model outperforms the traditional ST-GCN model, achieving an accuracy of 81.4%.

Dynamics modeling of urban rail vehicle axle-box bearings with roller defect considering time-varying impact

Accurately simulating the vibration response of the axle-box bearing with a localized defect on the roller can guide the proposal of several health condition estimation methods. However, there is still a lack of discussion and analysis on the detailed motion process of the roller defect passing through the inner/outer race. For filling this gap, a roller defect model considering time-varying impact is proposed and applied to the vehicle–track coupling system considering axle-box bearings. In addition to the Hertz contact force due to the relative displacement between the roller and race, time-varying impact excitation is designed according to the law of the conservation of energy. At first, an onsite test and an experimental test are implemented to validate the accuracy of the roller defect model and the vehicle–track coupling model, respectively. Then, the effect of the roller defect on the vibration characteristics of the components in the vehicle system is investigated. Finally, the influence of the defect size and vehicle speed on the vibration response of the axle-box bearing is studied, and the change rule of the time-varying impact is also researched with the defect size. Besides, the axle-box vibration response is simulated under variable vehicle speeds, and order tracking technique is applied to analyze the simulated result.

Towards Detailed Text-to-Motion Synthesis via Basic-to-Advanced Hierarchical Diffusion Model

Text-guided motion synthesis aims to generate 3D human motion that not only precisely reflects the textual description but reveals the motion details as much as possible. Pioneering methods explore the diffusion model for text-to-motion synthesis and obtain significant superiority. However, these methods conduct diffusion processes either on the raw data distribution or the low-dimensional latent space, which typically suffer from the problem of modality inconsistency or detail-scarce. To tackle this problem, we propose a novel Basic-to-Advanced Hierarchical Diffusion Model, named B2A-HDM, to collaboratively exploit low-dimensional and high-dimensional diffusion models for high quality detailed motion synthesis. Specifically, the basic diffusion model in low-dimensional latent space provides the intermediate denoising result that to be consistent with the textual description, while the advanced diffusion model in high-dimensional latent space focuses on the following detail-enhancing denoising process. Besides, we introduce a multi-denoiser framework for the advanced diffusion model to ease the learning of high-dimensional model and fully explore the generative potential of the diffusion model. Quantitative and qualitative experiment results on two text-to-motion benchmarks (HumanML3D and KIT-ML) demonstrate that B2A-HDM can outperform existing state-of-the-art methods in terms of fidelity, modality consistency, and diversity.

Novel evaluation method for facial nerve palsy using 3D facial recognition system in iPhone

ObjectiveWhile subjective methods like the Yanagihara system and the House-Brackmann system are standard in evaluating facial paralysis, they are limited by intra- and inter-observer variability. Meanwhile, quantitative objective methods such as electroneurography and electromyography are time-consuming. Our aim was to introduce a swift, objective, and quantitative method for evaluating facial movements. MethodsWe developed an application software (app) that utilizes the facial recognition functionality of the iPhone (Apple Inc., Cupertino, USA) for facial movement evaluation. This app leverages the phone's front camera, infrared radiation, and infrared camera to provide detailed three-dimensional facial topology. It quantitatively compares left and right facial movements by region and displays the movement ratio of the affected side to the opposite side. Evaluations using the app were conducted on both normal and facial palsy subjects and were compared with conventional methods. ResultsOur app provided an intuitive user experience, completing evaluations in under a minute, and thus proving practical for regular use. Its evaluation scores correlated highly with the Yanagihara system, the House-Brackmann system, and electromyography. Furthermore, the app outperformed conventional methods in assessing detailed facial movements. ConclusionOur novel iPhone app offers a valuable tool for the comprehensive and efficient evaluation of facial palsy.

Experimental and numerical studies on vertical water entry of a cylinder under the influence of current

When a moving object penetrates the free surface, it is often accompanied by complex multiphase flow phenomena. In this work, the complicated impact cavity and flow characteristics of water entry by a cylinder are investigated both experimentally and numerically for different current velocities. A high-speed photography technique is employed to capture the cavity contours under both calm water and flowing water. Under the same working conditions, a three-dimensional simulation using a volume of fraction model is conducted to provide detailed flow field structures and motion behaviors for experimental results. The primary results are as follows: the effect of current causes the cavities to no longer develop symmetrically. The cavity on the upstream side is compressed close to the cylinder surface, while the cavity on the downstream side expands. The asymmetrical distribution of cavities causes different wetting states on both sides of the cylinder. The ensuing pressure difference prompts the cylinder to deflect which is quite different that without considering current. Vortices generated on the upstream side disappear faster, while those generated on the downstream side expand significantly along the flow direction.

Inefficient Forecasts at the Sportsbook: An Analysis of Real-Time Betting Line Movement

This paper tests the efficiency of a set of sports betting markets using detailed betting line movement from opening until closing for four different sportsbooks for each of 3,681 Major League Baseball games. The reliability of the markets’ forecasts are assessed at several lead times. The forecasts are mostly reliable, but there are simple betting strategies that would have yielded significant profit. In addition, forecasts do not always improve monotonically as the games get closer, despite more information being available; forecasts at weekend day games’ start times are significantly worse than forecasts 90 minutes earlier. Furthermore, analysis of the sequences of forecasts within individual games reveals that these betting markets do not incorporate information optimally. There is sufficient evidence to reject weak form market efficiency; specifically, betting lines tend to overreact, exhibiting significant negatively autocorrelated changes that could be exploited by sophisticated bettors. This paper was accepted by Manel Baucells, behavioral economics and decision analysis. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.00456 .

Mechanically Interlocked Polymers with Dense Mechanical Bonds.

ConspectusMechanically interlocked polymers (MIPs) such as polyrotaxanes and polycatenanes are polymer architectures that incorporate mechanical bonds, which represent a compelling frontier in polymer science. MIPs with cross-linked structures are known as mechanically interlocked networks (MINs) and are widely utilized in materials science. Leveraging the motion of mechanical bonds, MINs hold the potential for achieving a combination of robustness and dynamicity. Currently, the reported MINs predominantly consist of networks with discrete mechanical bonds as cross-linking points, exemplified by well-known slide-ring materials and rotaxane/catenane cross-linked polymers. The motion of these mechanically interlocked cross-linking points facilitates the redistribution of tension throughout the network, effectively preventing stress concentration and thereby enhancing material toughness. In these instances, the impact of mechanical bonds can be likened to the adage "small things can make a big difference", whereby a limited number of mechanical bonds substantially elevate the mechanical performance of conventional polymers. In addition to MINs cross-linked by mechanical bonds, there is another type of MIN in which their principal parts are polymer chains composed of dense mechanical bonds. Within these MINs, mechanical bonds generally serve as repeating units, and their unique properties stem from integrating and amplifying the function of a large amount of mechanical bonds. Consequently, MINs with dense mechanical bonds tend to reflect the intrinsic properties of mechanical interlocked polymers, making their exploration critical for a comprehensive understanding of MIPs. Nevertheless, investigations into MINs featuring dense mechanical bonds remain relatively scarce.This Account presents a comprehensive overview of our investigation and insights into MINs featuring dense mechanical bonds. First, we delve into the synthetic strategies employed to effectively prepare MINs with dense mechanical bonds, while critically evaluating their advantages and limitations. Through meticulous control of the core interlocking step, three distinct strategies have emerged: mechanical interlocking followed by polymerization, supramolecular polymerization followed by mechanical interlocking, and dynamic interlocking. Furthermore, we underscore the structure-property relationships of MINs with dense mechanical bonds. The macroscopic properties of MINs originate from integrating and amplifying countless microscopic motions of mechanical bonds, a phenomenon we define as an integration and amplification mechanism. Our investigation has revealed detailed motion characteristics of mechanical bonds in bulk mechanically interlocked materials, encompassing the quantification of motion activation energy, discrimination of varying motion distances, and elucidation of the recovery process. Additionally, we have elucidated their influence on the mechanical performance of the respective materials. Moreover, we have explored potential applications of MINs, leveraging their exceptional mechanical properties and dynamicity. These applications include enhancing the toughness of conventional polymers, engineering mechanically adaptive and multifunctional aerogels, and mitigating Li protrusion as interfacial layers in lithium-ion batteries. Finally, we offer our personal perspectives on the promises, opportunities, and key challenges in the future development of MINs with dense mechanical bonds, underscoring the potential for transformative advancements in this burgeoning field.

Using Convolutional Neural Network to Design and Predict the Forces and Kinematic Performance and External Rotation Moment of the Hip Joint in the Pelvis

In order to improve the dynamic and kinematic adaptability of the hip joint, this paper presented a control attitude and kinematics and torque of the hip joint with power based neural network control. The CNN neural network uses input data only from the limb designed by the medical software, and is trained by different natural and artificially altered step patterns of healthy individuals. This type of network has been used for deep learning to realize adaptive speed control, dynamic and motion attitude, as well as prediction of force and torque performance. Detailed movement and torque tests were performed using MIMICS and ANATOMY AND PHYSIOLOGY software, and the obtained data were checked and varied by a healthy person, and finally, the test results showed that the neural network control system was able to control the selection. It has a variable and high speed with proper adaptation in various conditions. Finally, MATLAB software was used to design and predict the data of the problem, and favorable results were obtained.

Expert Performance in Action Anticipation: Visual Search Behavior in Volleyball Spiking Defense from Different Viewing Perspectives.

Volleyball spiking requires defenders to possess exceptional anticipatory skills. However, most volleyball defense video eye-tracking studies have used fixed or off-court perspectives, failing to replicate real-world environments. This study explored different visual search behaviors between elite and novice volleyball players from various viewing perspectives using video eye tracking. We examined spiking anticipation in 14 competitive elite, 13 semi-elite, and 11 novice players. We captured spiking videos from three on-court perspectives using GoPro cameras mounted on the defenders' heads, closely replicating real game scenarios. For comparison, we recorded baseline videos using a fixed camera. The present study revealed that competitive and semi-elite players demonstrated higher accuracy than novices. Competitive elite players used fewer fixations, indicating that their superior performance was related to stable visual search patterns. All participant groups, regardless of skill level, showed similar visual allocation among areas of interest (AOIs). However, notable differences in visual search patterns and AOI allocation were observed between baseline and on-court perspective videos. From the baseline perspective, the participants primarily utilized global perception and peripheral vision, focusing more on the setter zone or the spiker's trunk. Conversely, from the on-court perspective, they employed more fixations, focusing more intensely on the spiker's detailed movements.

Fall Risk Assessment in Stroke Survivors: A Machine Learning Model Using Detailed Motion Data from Common Clinical Tests and Motor-Cognitive Dual-Tasking.

Falls are common and dangerous for stroke survivors. Current fall risk assessment methods rely on subjective scales. Objective sensor-based methods could improve prediction accuracy. Develop machine learning models using inertial sensors to objectively classify fall risk in stroke survivors. Determine optimal sensor configurations and clinical test protocols. 21 stroke survivors performed balance, Timed Up and Go, 10 Meter Walk, and Sit-to-Stand tests with and without dual-tasking. A total of 8 motion sensors captured lower limb and trunk kinematics, and 92 spatiotemporal gait and clinical features were extracted. Supervised models-Support Vector Machine, Logistic Regression, and Random Forest-were implemented to classify high vs. low fall risk. Sensor setups and test combinations were evaluated. The Random Forest model achieved 91% accuracy using dual-task balance sway and Timed Up and Go walk time features. Single thorax sensor models performed similarly to multi-sensor models. Balance and Timed Up and Go best-predicted fall risk. Machine learning models using minimal inertial sensors during clinical assessments can accurately quantify fall risk in stroke survivors. Single thorax sensor setups are effective. Findings demonstrate a feasible objective fall screening approach to assist rehabilitation.

Are sucking patterns and early spontaneous movements related to later developmental functioning outcomes? A cohort study.

Sucking patterns and early spontaneous movements have an important role in the determination of later developmental problems, but the relationship of the two together with long-term outcomes has not been investigated. The objectives of this study were to (i) examine the relationship between sucking patterns using the Neonatal Oral Motor Assessment Scale (NOMAS) and fidgety movements and other movement patterns using detailed General Movements Assessment (GMA), and (ii) investigate the relationship between these early assessment methods and developmental functioning outcomes at later ages. We analyzed the NOMAS from 34weeks' postmenstrual age up to 10weeks post-term and GMA between 9 and 20weeks post-term age, and the Bayley Scales of Infant and Toddler Development-Third Edition (Bayley-III) was applied for the developmental functioning outcomes to 62 infants (61%, 62/102) between 12 and 42months of age. Among 102 infants at-risk, 70 (69%) showed a normal sucking pattern, and 85 (83%) had fidgety movements. The median Motor Optimality Score-Revised (MOS-R), as determined by GMA, of all infants was 24. The NOMAS was related to the MOS-R and its subcategories (p < 0.05) in all infants at-risk. The NOMAS, MOS-R and its subcategories were also related to cognitive, language, and motor development at later ages according to Bayley-III (p < 0.05). Conclusion: This longitudinal study showed that the quality of sucking patterns, fidgety movements, and MOS-R were related to later developmental functioning, indicating that abnormal sucking patterns, aberrant fidgety movements, and lower MOS-R might predict developmental disorders. What is Known: • Sucking patterns and early spontaneous movements in which central pattern generators play an important role are related. • Sucking patterns and early spontaneous movements might be used separately to predict developmental outcomes. What is New: • Sucking patterns and early spontaneous movements, when used together, were related to later developmental functioning, including cognitive, language, and motor developmentin at-risk infants. • Predictive value of sucking patterns was lower for each developmental functioning outcome than early spontaneous movements.

Investigation of thermo-hydrodynamic characteristics in micro-oscillating heat pipe by alternatively-arranged ratchet microchannels

In this study, three flat-plate micro-oscillating heat pipes (FPMOHPs) with different alternatively-arranged ratchet microchannels were designed and fabricated. Then, a combined visualization and thermal experiment was conducted to examine the regulation of thermo-hydrodynamic characteristics in the FPMOHPs by the alternatively-arranged ratchet microchannels, via the comparison with a traditional FPMOHP with completely uniform microchannel. The detailed fluid flow motions and corresponding temperature oscillations as well as thermal performance of the FPMOHPs were illustrated and analyzed to clarify the advantages of this microchannel design. It is indicated that the oscillating frequency and velocity of the fluid are diminished by the ratchet microchannels because of the higher flowing resistance. However, the ratchet microchannels can trigger the stronger flow disturbance, leading to the ascending “wave-like” annular flow broken into the dispersive slugs or bubbles with irregular shape. Among the three different ratchet microchannels, the asymmetric ratchet structure with the larger contraction-expansion ratio near the inlet of the fluid can trigger more intense boiling in the evaporator, which also energizes the downward flow from the condenser. Furthermore, the additional actions on the bubbles/slugs caused by the Laplace pressure jump in three different ratchet microchannels play distinct roles in upward and downward bubble/slug flow, i.e., driving force or resistance force or little role, which determine the most favorable ratchet structure for the fluid flow and heat transfer in the FPMOHPs. In general, the positive effects of the enhanced boiling and the stronger fluid flow disturbance caused by the ratchet microchannel dominates the negative effect of growing flow resistance, leading to the enhanced convective heat transfer. Therefore, the equivalent thermal conductivity can be improved up to 33.8 % on average as compared to the traditional FPMOHP with completely uniform rectangular channel.

Animatable Implicit Neural Representations for Creating Realistic Avatars From Videos.

This paper addresses the challenge of reconstructing an animatable human model from a multi-view video. Some recent works have proposed to decompose a non-rigidly deforming scene into a canonical neural radiance field and a set of deformation fields that map observation-space points to the canonical space, thereby enabling them to learn the dynamic scene from images. However, they represent the deformation field as translational vector field or SE(3) field, which makes the optimization highly under-constrained. Moreover, these representations cannot be explicitly controlled by input motions. Instead, we introduce blend weight fields to produce the deformation fields. Based on the skeleton-driven deformation, blend weight fields are used with 3D human skeletons to generate observation-to-canonical and canonical-to-observation correspondences. Since 3D human skeletons are more observable, they can regularize the learning of deformation fields. Moreover, the blend weight fields can be combined with input skeletal motions to generate new deformation fields to animate the human model. To improve the quality of human modeling, we further represent the human geometry as a signed distance field in the canonical space. Additionally, a neural point displacement field is introduced to enhance the capability of the blend weight field on modeling detailed human motions. Experiments show that our approach significantly outperforms recent human modeling methods.

Āsana for Back, Hips and Legs to Prevent Musculoskeletal Disorders among Dental Professionals: In-Office Yóga Protocol.

Specific Yoga positions (āsana, such as Virāsana, Virabhadrāsana, Garudāsana, Utkatāsana, Trikonāsana, Anuvittāsana, Chakrāsana, Uttanāsana, Pashimottanāsana) have been selected, elaborated on and adapted to be practiced in a dental office using a dental stool or the dental office walls or a dental unit chair. The protocol is specifically devised for dental professionals (dentists, dental hygienists and dental assistants) and targeted for the low back, hips and legs (including knees and ankles). The protocol includes Visranta Karaka Sthiti (supported positions) in sitting (Upavistha Sthiti) and standing (Utthistha Sthiti) positions, twisting/torsions (Parivrtta), flexions/forward bend positions (Pashima) and extensions/arching (Purva) for musculo-articular system decompression and mobilization. Over 60 Yogāsana-specifically ideated for back detensioning and mobilization, lumbar lordosis restoration, trunk side elongation, hip release and leg stretches and decontraction-are shown and described. The paper provides a meticulous description for each position, including the detailed movement, recommendations and mistakes to avoid, and the breathing pattern (breath control) in all the breath-driven movements (āsana in vinyāsa). An exhaustive analysis of posture-related disorders affecting the lower body among dental professionals is reported, including low-back pain, hip pain and disorders, piriformis syndrome and quadratus femoris dysfunction (gluteal pain), iliopsoas syndrome, multifidus disorders, femoroacetabular and ischiofemoral impingement, spinopelvic mobility, lumbopelvic rhythm, impairment syndromes, lower crossed syndrome, leg pain, knee pain and ankle disorders. A detailed guideline of āsana for low-back decompression, hip joint destress, piriformis and gluteal muscle release, lumbar lordosis recovery and a spinopelvic mobility increase has been elaborated on. The designed Yogāsana protocol represents a powerful tool for dental professionals to provide relief to retracted stiff muscles and unbalanced musculoskeletal structures in the lower body.

Using the center of pressure movement analysis in evaluating spontaneous movements in infants: a comparative study with general movements assessment

BackgroundResearchers have attempted to automate the spontaneous movement assessment and have sought quantitative and objective methods over the past decade. The purpose of the study was to present a quantitative assessment method of spontaneous movement using center-of-pressure (COP) movement analysis.MethodsA total of 101 infants were included in the study. The infants were placed in the supine position on the force plate with the cranial-caudal orientation. In this position, the recording of video and COP movement data were made simultaneously for 3 min. Video recordings were used to observe global and detailed general movement assessment (GMA), and COP time series data were used to obtain quantitative movement parameters.ResultsAccording to the global GMA, 13 infants displayed absent fidgety movements (FMs) and 88 infants displayed normal FMs. The binary logistic regression model indicated significant association between global GMA and COP movement parameters (chi-square = 20.817, p < 0.001). The sensitivity, specificity, and overall accuracy of this model were 85% (95% CI: 55–98), 83% (95% CI: 73–90), and 83% (95% CI: 74–90), respectively. The multiple linear regression model showed a significant association between detailed GMA (motor optimality score-revised/MOS-R) and COP movement parameters (F = 10.349, p < 0.001). The MOS-R total score was predicted with a standard error of approximately 1.8 points (6%).ConclusionsThe present study demonstrated the possible avenues for using COP movement analysis to objectively detect the absent FMs and MOS-R total score in clinical settings. Although the method presented in this study requires further validation, it may complement observational GMA and be clinically useful for infant screening purposes, particularly in clinical settings where access to expertise in observational GMA is not available.

Zebrafish Optokinetic Reflex: Minimal Reporting Guidelines and Recommendations.

Optokinetic reflex (OKR) assays in zebrafish models are a valuable tool for studying a diverse range of ophthalmological and neurological conditions. Despite its increasing popularity in recent years, there are no clear reporting guidelines for the assay. Following reporting guidelines in research enhances reproducibility, reduces bias, and mitigates underreporting and poor methodologies in published works. To better understand optimal reporting standards for an OKR assay in zebrafish, we performed a systematic literature review exploring the animal, environmental, and technical factors that should be considered. Using search criteria from three online databases, a total of 109 research papers were selected for review. Multiple crucial factors were identified, including larval characteristics, sample size, fixing method, OKR set-up, distance of stimulus, detailed stimulus parameters, eye recording, and eye movement analysis. The outcome of the literature analysis highlighted the insufficient information provided in past research papers and the lack of a systematic way to present the parameters related to each of the experimental factors. To circumvent any future errors and champion robust transparent research, we have created the zebrafish optokinetic (ZOK) reflex minimal reporting guideline.

Australia's east coast humpback whales: Satellite tag-derived movements on breeding grounds, feeding grounds and along the northern and southern migration.

Satellite tags were deployed on 50 east Australian humpback whales (breeding stock E1) between 2008 and 2010 on their southward migration, northward migration and feeding grounds in order to identify and describe migratory pathways, feeding grounds and possible calving areas. At the time, these movements were not well understood and calving grounds were not clearly identified. To the best of our knowledge, this dataset details all long-term, implantable tag deployments that have occurred to date on breeding stock E1. As such, these data provide researchers, regulators and industry with clear and valuable insights into the spatial and temporal nature of humpback whale movements along the eastern coastline of Australia and into the Southern Ocean. As this population of humpback whales navigates an increasingly complex habitat undergoing various development pressures and anthropogenic disturbances, in addition to climate-mediated changes in their marine environment, this dataset may also provide a valuable baseline. At the time these tracks were generated, these were the first satellite tag deployments intended to deliver long-term, detailed movement information on east Australian (breeding stock E1) humpback whales. The tracking data revealed previously unknown migratory pathways into the Southern Ocean, with 11 individuals tracked to their Antarctic feeding grounds. Once assumed to head directly south on their southern migration, five individuals initially travelled west towards New Zealand. Six tracks detailed the coastal movement of humpback whales migrating south. One tag transmitted a partial southern migration, then ceased transmissions only to begin transmitting eight months later as the animal was migrating north. Northern migration to breeding grounds was detailed for 13 individuals, with four tracks including turning points and partial southern migrations. Another 14 humpback whales were tagged in Antarctica, providing detailed Antarctic feeding ground movements.Broadly speaking, the tracking data revealed a pattern of movement where whales were at their northern limit in July and their southern limit in March. Migration north was most rapid across the months of May and June, whilst migration south was most rapid between November and December. Tagged humpback whales were located on their Antarctic feeding grounds predominantly between January and May and approached their breeding grounds between July and August. Tracking distances ranged from 68 km to 8580 km and 1 to 286 days. To the best of our knowledge, this dataset compiles all of the long-term tag deployments that have occurred to date on breeding stock E1.

Proximity-sensors on GPS collars reveal fine-scale predator-prey behavior during a predation event: A case study from Scandinavia.

Although the advent of high-resolution GPS tracking technology has helped increase our understanding of individual and multispecies behavior in wildlife systems, detecting and recording direct interactions between free-ranging animals remains difficult. In 2023, we deployed GPS collars equipped with proximity sensors (GPS proximity collars) on brown bears (Ursus arctos) and moose (Alces alces) as part of a multispecies interaction study in central Sweden. On 6 June, 2023, a collar on an adult female moose and a collar on an adult male bear triggered each other's UHF signal and started collecting fine-scale GPS positioning data. The moose collar collected positions every 2 min for 89 min, and the bear collar collected positions every 1 min for 41 min. On 8 June, field personnel visited the site and found a female neonate moose carcass with clear indications of bear bite marks on the head and neck. During the predation event, the bear remained at the carcass while the moose moved back and forth, moving toward the carcass site about five times. The moose was observed via drone with two calves on 24 May and with only one remaining calf on 9 June. This case study describes, to the best of our knowledge, the first instance of a predation event between two free ranging, wild species recorded by GPS proximity collars. Both collars successfully triggered and switched to finer-scaled GPS fix rates when the individuals were in close proximity, producing detailed movement data for both predator and prey during and after a predation event. We suggest that, combined with standard field methodology, GPS proximity collars placed on free-ranging animals offer the ability for researchers to observe direct interactions between multiple individuals and species in the wild without the need for direct visual observation.

Using pedestrian modelling to inform virus transmission mitigation policies: A novel activity scheduling model to enable virus transmission risk assessment in a restaurant environment

The Covid-19 pandemic has had a large impact on the world. The virus spreads especially easily among people in indoor spaces such as restaurants. Hence, tools that can assess how different restaurant settings can impact the potential spread of an airborne virus and that can assess the effectiveness of mitigation policies are of high value. Microscopic pedestrian models provide the tools necessary to assess the detailed movements of people in a restaurant and with that the risk of virus transmission. This paper presents the application of a microscopic pedestrian model, including a novel activity choice and scheduling model, to assess virus transmission risks in restaurants. Simulation experiments identify that different factors impact virus transmission risks in a restaurant. Contacts between restaurant staff and customers are the driving factor for virus transmission in a restaurant whereby especially staff presents a big risk. Hence, mitigation policies focussing on these interactions and on preventing staff from transmitting the virus can be highly effective. The results also show that different restaurant layouts and setups lead to distinctly different transmission risks. Therefore, insights obtained from simulating one restaurant cannot be just transferred to any other restaurant. Together, these results show the added value of including pedestrian models in disease transmission risk modelling exercises to mitigate the impact of a pandemic caused by an airborne virus. However, the research also shows that, to better utilize the potential of pedestrian models for disease transmission risk modelling, future research of pedestrian activity scheduling behaviour in indoor spaces is necessary.

The Importance of Representative Sampling for Home Range Estimation in Field Primatology

Understanding the amount of space required by animals to fulfill their biological needs is essential for comprehending their behavior, their ecological role within their community, and for effective conservation planning and resource management. The space-use patterns of habituated primates often are studied by using handheld GPS devices, which provide detailed movement information that can link patterns of ranging and space-use to the behavioral decisions that generate these patterns. However, these data may not accurately represent an animal’s total movements, posing challenges when the desired inference is at the home range scale. To address this problem, we used a 13-year dataset from 11 groups of white-faced capuchins (Cebus capucinus imitator) to examine the impact of sampling elements, such as sample size, regularity, and temporal coverage, on home range estimation accuracy. We found that accurate home range estimation is feasible with relatively small absolute sample sizes and irregular sampling, as long as the data are collected over extended time periods. Also, concentrated sampling can lead to bias and overconfidence due to uncaptured variations in space use and underlying movement behaviors. Sampling protocols relying on handheld GPS for home range estimation are improved by maximizing independent location data distributed across time periods much longer than the target species’ home range crossing timescale.