Evidence for persistent patch structure on temperate reefs and multiple hypotheses for their creation and maintenance

AbstractTwo kinds of marine polypropylene (M1-PP and M2-PP) and one land PP (L-PP) samples were collected from two beaches and land in Japan, respectively, to study the fragmentation mechanisms. Delamination was observed on both M1-PP and M2-PP surfaces. Moreover, there was no delamination but an abrasion patch structure on the surface of L-PP. The delamination was studied using an advanced oxidation process-degraded PP as the marine PP model. The number and shape of cracks varied with an increase in degradation time. The fluctuations in the values and ratios of the carbonyl index as well as the weight change ratio were due to repeated oxidation and delamination. We found that the delamination behavior depends on the oxidation state. Poly(oxyethylene)8 octylphenyl ether (POE8) surfactant treatment caused the delamination to speed up, which is a typical characteristic of polyolefin environmental stress cracking (ESC). These results reveal that delamination is based on ESC.Article Highlights Two kinds of marine and one land polypropylene (PP) samples were collected from two beaches and land, respectively, to study the fragmentation mechanisms. Delamination was observed on both of marine PP surfaces. Moreover, there was no delamination but an abrasion patch structure on the land PP surface. We found that the delamination was based on environmental stress cracking mechanism by employing a marine PP model.

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A Probabilistic Model for Real-Time Semantic Prediction of Human Motion Intentions from RGBD-Data

Sensors ◽

10.3390/s21124141 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4141

Author(s):

Wouter Houtman ◽

Gosse Bijlenga ◽

Elena Torta ◽

René van de Molengraft

Keyword(s):

Real Time ◽

Collision Avoidance ◽

Probabilistic Model ◽

Real Life ◽

Human Motion ◽

Model Based ◽

Multiple Hypotheses ◽

Navigation Algorithms ◽

The Right ◽

Motion Behavior

For robots to execute their navigation tasks both fast and safely in the presence of humans, it is necessary to make predictions about the route those humans intend to follow. Within this work, a model-based method is proposed that relates human motion behavior perceived from RGBD input to the constraints imposed by the environment by considering typical human routing alternatives. Multiple hypotheses about routing options of a human towards local semantic goal locations are created and validated, including explicit collision avoidance routes. It is demonstrated, with real-time, real-life experiments, that a coarse discretization based on the semantics of the environment suffices to make a proper distinction between a person going, for example, to the left or the right on an intersection. As such, a scalable and explainable solution is presented, which is suitable for incorporation within navigation algorithms.

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