Results 161 to 170 of about 5,348 (281)

Osteohistology of two phorusrhacids reveals uninterrupted growth strategy

open access: yesThe Anatomical Record, EarlyView.
Abstract Phorusrhacidae were apex predators that primarily dominated South America ecosystems for at least 40 million years with their imposing size and predatory lifestyle—yet some aspects of their biology remain poorly understood. Osteohistology is a tool for understanding growth dynamics and biomechanical adaptations.
Lotta Dreyer   +2 more
wiley   +1 more source

Legislative Proposals on Elimination of Gaps and Contradictions in the Regulation of Formation of Capital Repair’s Fund of Common Property in Apartment Buildings

open access: yesTyumen State University Herald. Social, Economic, and Law Research, 2017
Lyubov S. Kozlova   +2 more
openaire   +1 more source

A Review on the Research Progress of Imprint Film Materials for Nanoimprint Lithography. [PDF]

open access: yesMicromachines (Basel)
Yang Z   +8 more
europepmc   +1 more source

Over the edge: Empirical evidence for the cliff‐edge model of obstetric selection

open access: yesThe Anatomical Record, EarlyView.
Abstract The cliff‐edge model of obstetric selection maintains that larger neonates and smaller birth canals confer a positive selective advantage until labor becomes obstructed and vaginal delivery is no longer possible, eliciting an abrupt reduction in fitness.
Laura M. Watson   +6 more
wiley   +1 more source

Leveraging Big Multitemporal Multisource Satellite Data and Artificial Intelligence for the Detection of Complex and Invisible Features: The Case of Extensive Irrigation Mapping

open access: yesArchaeological Prospection, EarlyView.
ABSTRACT The detection of buried or obscured archaeological features remains a central challenge in landscape archaeology, particularly in the irrigated floodplains of Mesopotamia where levees and canals formed the basis of complex agrarian systems. This study presents a deep learning–based approach for the large‐scale, automated detection of ancient ...
Nazarij Buławka   +4 more
wiley   +1 more source

Risk‐aware safe reinforcement learning for control of stochastic linear systems

open access: yesAsian Journal of Control, EarlyView.
Abstract This paper presents a risk‐aware safe reinforcement learning (RL) control design for stochastic discrete‐time linear systems. Rather than using a safety certifier to myopically intervene with the RL controller, a risk‐informed safe controller is also learned besides the RL controller, and the RL and safe controllers are combined together ...
Babak Esmaeili   +2 more
wiley   +1 more source

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