The Origins of I-type Spherules and the Atmospheric Entry of Iron Micrometeoroids.

The Earth's extraterrestrial dust flux includes a wide variety of dust particles that include FeNi metallic grains. During their atmospheric entry iron micrometeoroids melt and oxidize to form cosmic spherules termed I-type spherules. These particles are chemically resistant and readily collected by magnetic separation and are thus the most likely micrometeorites to be recovered from modern and ancient sediments. Understanding their behavior during atmospheric entry is crucial in constraining their abundance relative to other particle types and the nature of the zodiacal dust population at 1 AU. This paper presents numerical simulations of the atmospheric entry heating of iron meteoroids in order to investigate the abundance and nature of these materials. The results indicate that iron micrometeoroids experience peak temperatures 300-800K higher than silicate particles explaining the rarity of unmelted iron particles which can only be present at sizes of <50 m. The lower evaporation rates of liquid iron oxide leads to greater survival of iron particles compared with silicates, which enhances their abundance amongst micrometeorites by a factor of 2. The abundance of I-types is shown to be broadly consistent with the abundance and size of metal in ordinary chondrites and the current day flux of ordinary chondrite-derived MMs arriving at Earth. Furthermore, carbonaceous asteroids and cometary dust are suggested to make negligible contributions to the I-type spherule flux. Events involving such objects, therefore, cannot be recognized from I-type spherule abundances in the geological record

The distribution of asteroids: evidence from Antarctic micrometeorites

The relative abundances of types amongst 550 AMMs are reported. These suggest that C-type asteroids vary from petrologic type 1 to 3.2 and that the majority of S-type asteroids are chondrule-rich

Term-based composition of security protocols

In the context of security protocol parallel composition, where messages belonging to different protocols can intersect each other, we introduce a new paradigm: term-based composition (i.e. the composition of message components also known as terms). First, we create a protocol specification model by extending the original strand spaces. Then, we provide a term composition algorithm based on which new terms can be constructed. To ensure that security properties are maintained, we introduce the concept of term connections to express the existing connections between terms and encryption contexts. We illustrate the proposed composition process by using two existing protocols.Comment: 2008 IEEE International Conference on Automation, Quality and Testing, Robotics, Cluj-Napoca, Romania, May 2008, pp. 233-238, ISBN 978-1-4244-2576-

LC-IRT models with covariates in Polish job satisfaction analysis

Employment is at the heart of European Union (EU) policies as it is the basis for wealth creation. Knowing how satisfied EU residents are with their occupation is very important, since losing one’s job may undermine one’s life satisfaction and its overall meaning (European Commission 2015). According to the most recent Eurostat data (European Commission 2017), Poland reported an average job satisfaction well above the EU mean, ranked 8th (behind Denmark, Iceland, Austria, Finland, Norway, Switzerland and Sweden). Thus, it is interesting to present an analysis focused on the job satisfaction of workers in Poland – a country of emigration, with the highest percentage of temporary contracts in Europe (European Commission 2016). The main aim of our study is understanding how the different socio-economic features affect the groups of workers with similar job satisfaction levels in Poland. Most of the Polish job satisfaction studies are focused on selected professional groups, in selected regions of Poland. This article presents another, the latent variable models approach to the hetero-geneous data set for different subgroups of workers in all the regions of Poland. The combination of the two latent variable models enables to find homogeneous classes of individuals characterized by the similar latent ability levels, and at the same time, the item characteristics analysis (usually identified as discrimination indices and difficulty parameters) as well. Latent Class Item Response Theory (LC-IRT) models are more flexible in compa-rison with traditional formulations of Item Response Theory (IRT) models, often based on restrictive assumptions, such as normality of latent trait (explicitly introduced). Moreover, the authors also apply the extended latent variable models under the discrete assumption of the latent trait including individual socio-demographic features, such as age, sex, education, marital status or current financial situation. The article analyzes data collected as part of the International Social Survey Programme 2015 using R software. The results may help policymakers tailor their employment policies as well as to create and deliver services focused on special socio-economic groups of the Polish society