Binders for Nonwoven Fabrics. Evaluation of Binders Based on Copolymers of Vinyl Acetate

Adelman, Robert L.; Allen, Geoffrey; Sinclair, Hannah

doi:10.1021/i360006a005

Cited by 8 publications

(2 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Understanding animal movement has advanced considerably in recent decades with the emergence of the field of movement ecology (Fraser et al 2018; Joo et al 2020), which focuses on understanding where animals go, what they are doing, and how they are influenced by their surrounding environment (Nathan et al 2008; Seidel et al 2018). As telemetry and biologging devices continue to increase in their battery life, data resolution, and affordability (Hussey et al 2015; Kays et al 2015; Wilmers et al 2015), statistical methods that can efficiently analyze these large datasets will become ever more important (Patterson et al 2017; Potts et al 2018).…”

Section: Introductionmentioning

confidence: 99%

“…As telemetry and biologging devices continue to increase in their battery life, data resolution, and affordability (Hussey et al 2015; Kays et al 2015; Wilmers et al 2015), statistical methods that can efficiently analyze these large datasets will become ever more important (Patterson et al 2017; Potts et al 2018). To fully understand animal movement, it is necessary to account for behavior since space and resource use are directly linked to an animal’s internal state (Nathan et al 2008; Gurarie et al 2016).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identifying latent behavioral states in animal movement with M4, a non-parametric Bayesian method

Cullen

Poli

Fletcher

et al. 2020

Preprint

View full text Add to dashboard Cite

1. Understanding animal movement often relies upon telemetry and biologging devices. These data are frequently used to estimate latent behavioral states to help understand why animals move across the landscape (or seascape). While there are a variety of methods that make behavioral inference from biotelemetry data, some features of these methods (e.g., analysis of a single data stream, use of parametric distributions) may result in the misclassification of behavioral states. 2. We address some of the limitations of segmentation and state-space models (SSMs) in our non-parametric Bayesian framework, available within the open-source R package bayesmove. This framework can analyze multiple data streams, which may capture complex behaviors more successfully than a single data stream. Additionally, parametric distributions are not used in our framework since they may poorly characterize the underlying true distributions. We tested our Bayesian framework using simulated trajectories and compared model performance against a representative segmentation method (behavioral change point analysis; BCPA) and one type of SSM, a hidden Markov model (HMM). We also illustrated this Bayesian framework using movements of juvenile snail kites (Rostrhamus sociabilis) in Florida, USA. 3. The Bayesian framework estimated breakpoints more accurately than BCPA for tracks of different lengths, albeit at a slower computational speed. Likewise, the Bayesian framework provided more accurate estimates of behavior than the HMM when simulations were generated from atypical distributions. This framework also performed up to three times faster than the HMM when run in a similar fashion. Three behavioral states were estimated from snail kite movements, which were labeled as encamped, area-restricted search, and transit. Changes in these behaviors over time were associated with known dispersal events from the nest site, as well as movements to and from possible breeding locations. 4. Our framework estimated behavioral states with comparable or superior accuracy compared to BCPA and HMM when step lengths and turning angles of simulations were generated from atypical distributions. Since empirical data can be complex and do not necessarily conform to parametric distributions, methods (such as our Bayesian framework) that can flexibly classify latent behavioral states will become increasingly important to address questions in movement ecology.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identifying latent behavioral states in animal movement with M4, a non-parametric Bayesian method

Cullen

Poli

Fletcher

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

How territoriality and sociality influence the habitat selection and movements of a large carnivore

Hansen,

Ranc,

Morgan

et al. 2024

Ecology and Evolution

View full text Add to dashboard Cite

While territoriality is one of the key mechanisms influencing carnivore space use, most studies quantify resource selection and movement in the absence of conspecific influence or territorial structure. Our analysis incorporated social information in a resource selection framework to investigate mechanisms of territoriality and intra‐specific competition on the habitat selection of a large, social carnivore. We fit integrated step selection functions to 3‐h GPS data from 12 collared African wild dog packs in the Okavango Delta and estimated selection coefficients using a conditional Poisson likelihood with random effects. Packs selected for their neighbors' 30‐day boundary (defined as their 95% kernel density estimate) and for their own 90‐day core (defined as their 50% kernel density estimate). Neighbors' 30‐day boundary had a greater influence on resource selection than any habitat feature. Habitat selection differed when they were within versus beyond their neighbors' 30‐day boundary. Pack size, pack tenure, pup presence, and seasonality all mediated how packs responded to neighbors' space use, and seasonal dynamics altered the strength of residency. While newly‐formed packs and packs with pups avoided their neighbors' boundary, older packs and those without pups selected for it. Packs also selected for the boundary of larger neighboring packs more strongly than that of smaller ones. Social structure within packs has implications for how they interact with conspecifics, and therefore how they are distributed across the landscape. Future research should continue to investigate how territorial processes are mediated by social dynamics and, in turn, how territorial structure mediates resource selection and movement. These results could inform the development of a human–wildlife conflict (HWC) mitigation tool by co‐opting the mechanisms of conspecific interactions to manage space use of endangered carnivores.

show abstract

The Mechanism of Adhesion of Phenol-Formaldehyde Resins to Cellulosic and Lignocellulosic Substrates

Allan

Neogi

1971

The Journal of Adhesion

View full text Add to dashboard Cite

The mechanism of adhesion of phenolic resins to cellulosic and lignocellulosic fibers has been explored using a nonpolymerizable bromine-labelled benzyl alcohol as a model for the adhesive. Bromine analysis by neutron activation techniques is sufficiently sensitive to show that reaction occurs much more extensively with the lignified fibers. This is attributed to the formation of covalent linkages between the model adhesive and the guaiacyl units in the lignin. The excellent general performance of phenolic adhesives on cellulose and lignocellulosic substrates can therefore be in part ascribed to the existence of high enthalpy covalent chemical bonds between adherend and adhesive.

show abstract

Binders for Nonwoven Fabrics. Evaluation of Binders Based on Copolymers of Vinyl Acetate

Cited by 8 publications

References 1 publication

Identifying latent behavioral states in animal movement with M4, a non-parametric Bayesian method

Identifying latent behavioral states in animal movement with M4, a non-parametric Bayesian method

How territoriality and sociality influence the habitat selection and movements of a large carnivore

The Mechanism of Adhesion of Phenol-Formaldehyde Resins to Cellulosic and Lignocellulosic Substrates

Contact Info

Product

Resources

About