Generalized Clifford Algebras as Algebras in Suitable Symmetric Linear Gr-Categories

By viewing Clifford algebras as algebras in some suitable symmetric Gr-categories, Albuquerque and Majid were able to give a new derivation of some well known results about Clifford algebras and to generalize them. Along the same line, Bulacu observed that Clifford algebras are weak Hopf algebras in the aforementioned categories and obtained other interesting properties. The aim of this paper is to study generalized Clifford algebras in a similar manner and extend the results of Albuquerque, Majid and Bulacu to the generalized setting. In particular, by taking full advantage of the gauge transformations in symmetric linear Gr-categories, we derive the decomposition theorem and provide categorical weak Hopf structures for generalized Clifford algebras in a conceptual and simpler manner

Elementary Particles and Plasma in the First Hour of the Early Universe

This dissertation aims to deepen the understanding of the primordial composition of the Universe in the temperature range 300 MeV>T>0.02 MeV. I exploit known properties of elementary particles and apply methods of kinetic theory and statistical physics to advance the understanding of the cosmic plasma. Within the Big Bang model, we begin by considering the Universe being a highly energetic fireball, an ultra-relativistic plasma exhibiting distinct properties. Fundamental particles such as quarks, leptons, and even heavier gauge bosons play a crucial role in the understanding of the early Universe. Our research focuses on the investigation of these fundamental particles as constituents of the dense Universe plasma during the epoch which transits from primordial quark-gluon plasma to the era of normal hadron matter, passing through the decoupling of neutrinos and addressing in detail the electron-positron antimatter plasma.Comment: PhD thesis, 150 pages, 31 figures. Includes work done in collaboration with Andrew Steinmetz, Christopher Grayson, Martin Formanek, Jeremiah Birrell, and Johann Rafelski Martin Formanek, Cheng Tao Yang, and Johann Rafelsk

Inferring Social-Demographics of Travellers based on Smart Card Data

[EN] With the wide application of the smart card technology in public transit system, traveller’s daily travel behaviours can be possibly obtained. This study devotes to investigating the pattern of individual mobility patterns and its relationship with social-demographics. We first extract travel features from the raw smart card data, including spatial, temporal and travel mode features, which capture the travel variability of travellers. Then, travel features are fed to various supervised machine learning models to predict individual’s demographic attributes, such as age group, gender, income level and car ownership. Finally, a case study based on London’s Oyster Card data is presented and results show it is a promisingZhang, Y.; Cheng, T. (2018). Inferring Social-Demographics of Travellers based on Smart Card Data. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 55-62. https://doi.org/10.4995/CARMA2018.2018.8310OCS556