Carina’s pillars of destruction: the view from ALMA

ABSTRACT Forming high-mass stars have a significant effect on their natal environment. Their feedback pathways, including winds, outflows, and ionizing radiation, shape the evolution of their surroundings which impacts the formation of the next generation of stars. They create or reveal dense pillars of gas and dust towards the edges of the cavities they clear. They are modelled in feedback simulations, and the sizes and shapes of the pillars produced are consistent with those observed. However, these models predict measurably different kinematics which provides testable discriminants. Here we present the first ALMA Compact Array (ACA) survey of 13 pillars in Carina, observed in 12CO, 13CO, and C18O J = 2–1, and the 230 GHz continuum. The pillars in this survey were chosen to cover a wide range in properties relating to the amount and direction of incident radiation, proximity to nearby irradiating clusters and cloud rims, and whether they are detached from the cloud. With these data, we are able to discriminate between models. We generally find pillar velocity dispersions of <1 km s−1 and that the outer few layers of molecular emission in these pillars show no significant offsets from each other, suggesting little bulk internal motions within the pillars. There are instances where the pillars are offset in velocity from their parental cloud rim, and some with no offset, hinting at a stochastic development of these motions.

A STOCHASTIC DEVELOPMENT OF CLOUD COMPUTING BASED TASK SCHEDULING ALGORITHM

Due to diversity of services with respect to technology and resources, it is challenging to choose virtual machines (VM) from various data centres with varied features like cost minimization, reduced energy consumption, optimal response time and so on in cloud Infrastructure as a Service (IaaS) environment. The solutions available in the market are exhaustive computationally and aggregates multiple objectives to procure single trade-off that affects the solution quality inversely. This paper describes a hybrid algorithm that facilitates VM selection for scheduling applications based on Gravitational Search and Non-dominated Sorting Genetic Algorithm (GSA and NSGA). The efficiency of the proposed algorithm is verified by the simulation results.

Mathematical and Stochastic Models for Reliability in Repairable Industrial Physical Assets

Generally, assets present a varied behaviour in their life cycle, which is related directly to the use given and consequently related to the technical assistance traditionally known as maintenance or maintenance policies. It can be of a diverse nature: perfect, minimum, imperfect, over-perfect, and destructive as appropriate. This feature requires the application of advanced techniques in order to model the behaviour of assets life, adapting ideally to each reality of use and wear out. In this chapter, the stochastic models PRP, NHPP, and GRP are explained with their conceptual, mathematic, and stochastic development. For each model, the conceptualization, parameterizing, and stochastic simulation are analysed. Additionally, complementing the analysis and resolution pattern, these models are concluded with a numeric application that allows one to show step by step the mathematic and stochastic development as appropriate.