Functional aspects of titanosaur osteoderms

Though titanosaur osteoderms are not common findings, these elements are recorded widely in Gondwana and part of Laurasia. This assembly known by the date offers few resources for studies on the ecology of this group of dinosaurs. Recently, some eggs bearing titanosaur embryos with preserved skin, from Patagonia, Argentina, may shed some light on the function and disposition pattern of these dermic bones. Some of the skin patches associated with the titanosaur embryos show two distinct patterns of tuberosities: a longitudinal row and rosettes, both composed of closely attached tuberosities. These tuberosities do not seem to be ossified, but this might be due to the ontogenetic stage of the titanosaurs. Here we propose that these tuberosities might have been ossified in later ontogenetic stages, and then turning into real osteoderms providing physical defence for the juvenile titanosaurs. Amongst the remains of large titanosaurs like _Mendozasaurus neguyelap_ some osteoderms were recovered, but it is contrasting small to the animal when compared to other dinosaurs. These bones would not provide real defensive advantage to an adult titanosaur as they are small and also have a very spongy internal structure. This apparent fragility also may be the reason that few titanosaur osteoderms have been preserved. In comparison, the tuberosity of a young titanosaur is much larger than an adult osteoderm and its body armor would be much more effective against small predators like notosuchian crocodyliforms and small theropods. And if the titanosaur osteoderms are originated from the embryo’s tuberosities, the disposition of these elements in an adult animal would be very distant to each other. The calcium reserve may be considered as functional for an adult titanosaur though

Timing Analysis for DAG-based and GFP Scheduled Tasks

Modern embedded systems have made the transition from single-core to multi-core architectures, providing performance improvement via parallelism rather than higher clock frequencies. DAGs are considered among the most generic task models in the real-time domain and are well suited to exploit this parallelism. In this paper we provide a schedulability test using response-time analysis exploiting exploring and bounding the self interference of a DAG task. Additionally we bound the interference a high priority task has on lower priority ones

Distinct dynamical behavior in Erd\H{o}s-R\'enyi networks, regular random networks, ring lattices, and all-to-all neuronal networks

Neuronal network dynamics depends on network structure. In this paper we study how network topology underpins the emergence of different dynamical behaviors in neuronal networks. In particular, we consider neuronal network dynamics on Erd\H{o}s-R\'enyi (ER) networks, regular random (RR) networks, ring lattices, and all-to-all networks. We solve analytically a neuronal network model with stochastic binary-state neurons in all the network topologies, except ring lattices. Given that apart from network structure, all four models are equivalent, this allows us to understand the role of network structure in neuronal network dynamics. Whilst ER and RR networks are characterized by similar phase diagrams, we find strikingly different phase diagrams in the all-to-all network. Neuronal network dynamics is not only different within certain parameter ranges, but it also undergoes different bifurcations (with a richer repertoire of bifurcations in ER and RR compared to all-to-all networks). This suggests that local heterogeneity in the ratio between excitation and inhibition plays a crucial role on emergent dynamics. Furthermore, we also observe one subtle discrepancy between ER and RR networks, namely ER networks undergo a neuronal activity jump at lower noise levels compared to RR networks, presumably due to the degree heterogeneity in ER networks that is absent in RR networks. Finally, a comparison between network oscillations in RR networks and ring lattices shows the importance of small-world properties in sustaining stable network oscillations.Comment: 9 pages, 4 figure